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Chen-Plotkin, Alice S. "Blood transcriptomics for Parkinson disease?" Nature Reviews Neurology 14, no. 1 (December 1, 2017): 5–6. http://dx.doi.org/10.1038/nrneurol.2017.166.
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Ronza, Paolo, José Antonio Álvarez-Dios, Diego Robledo, Ana Paula Losada, Roberto Romero, Roberto Bermúdez, Belén G. Pardo, Paulino Martínez, and María Isabel Quiroga. "Blood Transcriptomics of Turbot Scophthalmus maximus: A Tool for Health Monitoring and Disease Studies." Animals 11, no. 5 (April 30, 2021): 1296. http://dx.doi.org/10.3390/ani11051296.
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Blood transcriptomics is emerging as a relevant tool to monitor the status of the immune system and assist in diagnosis, prognosis, treatment and pathogenesis studies of diseases. In fish pathology, the potential of transcriptome profiling of blood is still poorly explored. Here, RNA sequencing was applied to analyze the blood transcriptional profile of turbot (Scophthalmus maximus), the most important farmed flatfish. The study was conducted in healthy specimens and specimens parasitized by the myxozoan Enteromyxum scophthalmi, which causes one of the most devastating diseases in turbot aquaculture. The blood of healthy turbot showed a transcriptomic profile mainly related to erythrocyte gas transportation function, but also to antigen processing and presentation. In moderately infected turbot, the blood reflected a broad inhibition of the immune response. Particularly, down-regulation of the B cell receptor signaling pathway was shared with heavily parasitized fish, which showed larger transcriptomic changes, including the activation of the inflammatory response. Turbot response to enteromyxosis proved to be delayed, dysregulated and ineffective in stopping the infection. The study evinces that blood transcriptomics can contribute to a better understanding of the teleost immune system and serve as a reliable tool to investigate the physiopathological status of fish.
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Staratschek-Jox, Andrea, Sabine Classen, Andrea Gaarz, Svenja Debey-Pascher, and Joachim L. Schultze. "Blood-based transcriptomics: leukemias and beyond." Expert Review of Molecular Diagnostics 9, no. 3 (April 2009): 271–80. http://dx.doi.org/10.1586/erm.09.9.
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Zhou, Jie, Bing Liu, and Yu Lan. "When blood development meets single-cell transcriptomics." Blood Science 1, no. 1 (August 2019): 65–68. http://dx.doi.org/10.1097/bs9.0000000000000007.
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Li, Shuzhao, Andrei Todor, and Ruiyan Luo. "Blood transcriptomics and metabolomics for personalized medicine." Computational and Structural Biotechnology Journal 14 (2016): 1–7. http://dx.doi.org/10.1016/j.csbj.2015.10.005.
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Williams, Cameron Gerard, Jessica A. Engel, Megan S. F. Soon, Evan Murray, Fei Chen, and Ashraful Haque. "Studying lymphocyte differentiation in the spleen via spatial transcriptomics." Journal of Immunology 206, no. 1_Supplement (May 1, 2021): 98.55. http://dx.doi.org/10.4049/jimmunol.206.supp.98.55.
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Abstract Immune cell positioning within secondary lymphoid tissues likely affects cell-cell interaction and subsequent immune responses. Techniques such as intra-vital imaging and multiplex immunohistochemistry provide insight into this, but require specialist reagents. To examine cell-cell interactions in dense tissue at whole-genome scale, we tested the feasibility of a single-cell spatial transcriptomics method, Slide-seq2. We first confirmed using murine gut tissue that small tertiary lymphoid structures, particularly rich in B cells, could be identified and examined at a cellular level in the small intestine. We then hypothesized that microanatomical alterations could be detected. To test this, we compared mouse spleens before and 7 days after infection with blood-stage malaria parasites. To increase the molecular resolution of our data, we integrated Slide-seq2 data with high-depth, droplet-based, scRNA-seq data, generated via Chromium controller from 10× Genomics. We found that Slide-seq2 produced sufficiently rich data to map cell types from an scRNA-seq reference. Some spatially defined transcriptomes appeared to derive from mixtures of cell types, indicating that further deconvolution of spatially resolved transcriptomic data was required. Via unsupervised clustering of whole transcriptomes, we confirmed that T and B cell zones within naïve mice became less ordered at the peak of malaria infection, reflecting T and B cell interaction. Ongoing analyses aim to define novel splenic T and B cell interactions during malaria, both in extra follicular areas as well as within the germinal center.
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Dong, Ruochen, Jonathon Russell, Seth Malloy, Kate Hall, Sarah E. Smith, Hua Li, Yongfu Wang, et al. "Using Spatial Transcriptomics to Reveal Fetal Liver Hematopoietic Stem Cell-Niche Interactions." Blood 138, Supplement 1 (November 5, 2021): 3284. http://dx.doi.org/10.1182/blood-2021-153748.
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Abstract The hematopoietic stem cell (HSC) microenvironment, termed the niche, supports the proliferation, self-renewal, and differentiation abilities of HSCs. The definitive HSCs emerge from the hemogenic endothelium in the aorta-gonad-mesonephros (AGM) region after E11.5, and then migrate to the fetal liver after E12.5 for expansion. After E17.5, HSCs migrate to the bone marrow and reside in the bone marrow for the postnatal stage and adulthood. Because the fetal liver is thought to be a harbor for the rapid expansion of HSCs, numerous studies have focused on the fetal liver HSC niche in the search for novel niche factors and niche cells that support HSC expansion. However, to our knowledge, there are no successes in translating the niche factors to a clinical application for the expansion of HSCs ex vivo. In this study, we are using cutting-edge spatial transcriptomics to comprehensively study the transcriptomics and interactions between HSCs and the niche cells in the fetal liver, and in search of the niche cells and factors for HSC expansion. To understand the spatial distribution and interactions between HSCs and niche cells in the fetal liver, we introduced 2 spatial transcriptomic methods, slide-seq, and 10x Visium, in our study on E14.5 mouse fetal liver. By integrating with a parallel single-cell sequencing analysis, we revealed the spatial transcriptomics of HSCs and potential niche cells, including hepatoblasts, endothelium cells, macrophages, megakaryocytes, and hepatic stellate cells/perivascular mesenchymal cells (PMCs) in E14.5 mouse fetal liver. Interestingly, we found that the PMCs were characterized by enriched N-cadherin expression. Both slide-seq and 10x Visium showed that the N-cadherin-expressing PMCs are enriched in the portal vessel area. Importantly, the majority of fetal liver HSCs are in close proximity to N-cadherin-expressing PMCs, indicating a supportive role of N-cadherin-expressing PMCs in HSC maintenance. Subsequent CellPhoneDB (CPDB) analysis demonstrated that the N-cadherin-expressing PMCs are major niche-signaling senders with an enriched expression of niche factors, such as CXCL12 and KITL, and stemness pathway-related ligands, such as IGF1, IGF2, TGFβ2, TGFβ3, JAG2, and DLK1, indicating N-cadherin-expressing PMCs could be the major niche cells in supporting HSCs in the fetal liver. This finding was consistent with our previous finding that N-cadherin-expressing bone and marrow stromal progenitor cells can maintain reserve HSCs in the adult bone marrow. Moreover, CPDB analysis indicated that other potential niche cells, such as endothelium cells, macrophages, and megakaryocytes, may support HSCs in different signal transduction pathways. For example, endothelium cells have an enriched expression of KITL, IGF2, DLL1, TGFβ1, and TGFβ2; macrophages have enriched expression of KITL, IFNγ, and TGFβ1; megakaryocytes have enriched expression of PF4, JAG2 and TGFβ1. Intriguingly, our previous studies showed that megakaryocytes could promote HSC expansion under stress conditions in the bone marrow. To investigate the potential role of N-cadherin-expressing cells in supporting fetal liver HSCs, we generated an N-cad CreER;Cxcl12 and an N-cad CreER;Scf mouse model to conditionally knockout the well-studied niche factors, CXCL12 and SCF, in N-cadherin-expressing cells. Conditional knockout of either Cxcl12 or Scf in N-cadherin-expressing cells resulted in an increase in the number of HSCs. Moreover, conditional knockout of Cxcxl12 in N-cadherin-expressing cells also resulted in a myeloid-biased differentiation. We postulate that the knockout of Cxcl12 or Scf in N-cadherin-expressing cells leads to the migration of HSCs towards other potential niche cells, such as macrophages and megakaryocytes, which may induce HSC expansion and biased differentiation. In summary, by using cutting-edge spatial transcriptomics, we revealed a comprehensive spatial transcriptomics of HSCs and niche cells in E14.5 mouse fetal liver. The N-cadherin-expressing cells in the fetal liver is a major niche in maintaining HSCs, while other potential niches may be responsible for the expansion of HSCs. In the future, we will use multiple approaches, such as spatial transcriptomics and fluorescence in situ hybridization (FISH), to verify the distribution changes of HSCs in N-cad CreER;Cxcl12 mouse, and to reveal the niches in support of the expansion of HSCs. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Pantaleo, Ester, Alfonso Monaco, Nicola Amoroso, Angela Lombardi, Loredana Bellantuono, Daniele Urso, Claudio Lo Lo Giudice, et al. "A Machine Learning Approach to Parkinson’s Disease Blood Transcriptomics." Genes 13, no. 5 (April 21, 2022): 727. http://dx.doi.org/10.3390/genes13050727.
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The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.
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GRIGORYEV, D., T. WATKINS, L. GAO, A. GRANT, M. STOCKTONPORTER, H. WATSON, R. MATHIAS, M. GITTENS, C. CHEADLE, and K. BARNES. "Transcriptomics of Peripheral Blood from Asthmatics Living in Barbados." Journal of Allergy and Clinical Immunology 121, no. 2 (February 2008): S259. http://dx.doi.org/10.1016/j.jaci.2007.12.1027.
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Blanchard, E. M., S. Domhan, L. Ma, C. Schwager, S. Ambika, L. A. Martin, J. Debus, P. J. Hesketh, L. Hlatky, and A. Abdollahi. "Peripheral blood transcriptomics-based molecular predictors of breast cancer." Journal of Clinical Oncology 28, no. 15_suppl (May 20, 2010): e21018-e21018. http://dx.doi.org/10.1200/jco.2010.28.15_suppl.e21018.
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Alhamdan, Fahd, Kristina Laubhahn, Christine Happle, Anika Habener, Adan C. Jirmo, Clemens Thölken, Raffaele Conca, et al. "Timing of Blood Sample Processing Affects the Transcriptomic and Epigenomic Profiles in CD4+ T-cells of Atopic Subjects." Cells 11, no. 19 (September 22, 2022): 2958. http://dx.doi.org/10.3390/cells11192958.
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Optimal pre-analytical conditions for blood sample processing and isolation of selected cell populations for subsequent transcriptomic and epigenomic studies are required to obtain robust and reproducible results. This pilot study was conducted to investigate the potential effects of timing of CD4+ T-cell processing from peripheral blood of atopic and non-atopic adults on their transcriptomic and epigenetic profiles. Two heparinized blood samples were drawn from each of three atopic and three healthy individuals. For each individual, CD4+ T-cells were isolated from the first blood sample within 2 h (immediate) or from the second blood sample after 24 h storage (delayed). RNA sequencing (RNA-Seq) and histone H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-Seq) analyses were performed. A multiplicity of genes was shown to be differentially expressed in immediately processed CD4+ T-cells from atopic versus healthy subjects. These differences disappeared when comparing delayed processed cells due to a drastic change in expression levels of atopy-related genes in delayed processed CD4+ T-cells from atopic donors. This finding was further validated on the epigenomic level by examining H3K27 acetylation profiles. In contrast, transcriptomic and epigenomic profiles of blood CD4+ T-cells of healthy donors remained rather unaffected. Taken together, for successful transcriptomics and epigenomics studies, detailed standard operation procedures developed on the basis of samples from both healthy and disease conditions are implicitly recommended.
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Gnatenko, Dmitri V., Peter L. Perrotta, and Wadie F. Bahou. "Proteomic approaches to dissect platelet function: half the story." Blood 108, no. 13 (December 15, 2006): 3983–91. http://dx.doi.org/10.1182/blood-2006-06-026518.
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AbstractPlatelets play critical roles in diverse hemostatic and pathologic disorders and are broadly implicated in various biological processes that include inflammation, wound healing, and thrombosis. Recent progress in high-throughput mRNA and protein profiling techniques has advanced our understanding of the biological functions of platelets. Platelet proteomics has been adopted to decode the complex processes that underlie platelet function by identifying novel platelet-expressed proteins, dissecting mechanisms of signal or metabolic pathways, and analyzing functional changes of the platelet proteome in normal and pathologic states. The integration of transcriptomics and proteomics, coupled with progress in bioinformatics, provides novel tools for dissecting platelet biology. In this review, we focus on current advances in platelet proteomic studies, with emphasis on the importance of parallel transcriptomic studies to optimally dissect platelet function. Applications of these global profiling approaches to investigate platelet genetic diseases and platelet-related disorders are also addressed.
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Conway, Bryan R., Eoin D. O’Sullivan, Carolynn Cairns, James O’Sullivan, Daniel J. Simpson, Angela Salzano, Katie Connor, et al. "Kidney Single-Cell Atlas Reveals Myeloid Heterogeneity in Progression and Regression of Kidney Disease." Journal of the American Society of Nephrology 31, no. 12 (September 25, 2020): 2833–54. http://dx.doi.org/10.1681/asn.2020060806.
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BackgroundLittle is known about the roles of myeloid cell subsets in kidney injury and in the limited ability of the organ to repair itself. Characterizing these cells based only on surface markers using flow cytometry might not provide a full phenotypic picture. Defining these cells at the single-cell, transcriptomic level could reveal myeloid heterogeneity in the progression and regression of kidney disease.MethodsIntegrated droplet– and plate-based single-cell RNA sequencing were used in the murine, reversible, unilateral ureteric obstruction model to dissect the transcriptomic landscape at the single-cell level during renal injury and the resolution of fibrosis. Paired blood exchange tracked the fate of monocytes recruited to the injured kidney.ResultsA single-cell atlas of the kidney generated using transcriptomics revealed marked changes in the proportion and gene expression of renal cell types during injury and repair. Conventional flow cytometry markers would not have identified the 12 myeloid cell subsets. Monocytes recruited to the kidney early after injury rapidly adopt a proinflammatory, profibrotic phenotype that expresses Arg1, before transitioning to become Ccr2+ macrophages that accumulate in late injury. Conversely, a novel Mmp12+ macrophage subset acts during repair.ConclusionsComplementary technologies identified novel myeloid subtypes, based on transcriptomics in single cells, that represent therapeutic targets to inhibit progression or promote regression of kidney disease.
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Watcham, Sam, Iwo Kucinski, and Berthold Gottgens. "New insights into hematopoietic differentiation landscapes from single-cell RNA sequencing." Blood 133, no. 13 (March 28, 2019): 1415–26. http://dx.doi.org/10.1182/blood-2018-08-835355.
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Abstract Single-cell transcriptomics has recently emerged as a powerful tool to analyze cellular heterogeneity, discover new cell types, and infer putative differentiation routes. The technique has been rapidly embraced by the hematopoiesis research community, and like other technologies before, single-cell molecular profiling is widely expected to make important contributions to our understanding of the hematopoietic hierarchy. Much of this new interpretation relies on inference of the transcriptomic landscape as a representation of existing cellular states and associated transitions among them. Here we review how this model allows, under certain assumptions, charting of time-resolved differentiation trajectories with unparalleled resolution and how the landscape of multipotent cells may be rather devoid of discrete structures, challenging our preconceptions about stem and progenitor cell types and their organization. Finally, we highlight how promising technological advances may convert static differentiation landscapes into a dynamic cell flux model and thus provide a more holistic understanding of normal hematopoiesis and blood disorders.
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Candelli, Tito, Pauline Schneider, Patricia Garrido Castro, Luke A. Jones, Rob Pieters, Thanasis Margaritis, Ronald W. Stam, and Frank C. P. Holstege. "Single Cell Transcriptomics Predicts Relapse in Infants with Acute Lymphoblastic Leukemia." Blood 134, Supplement_1 (November 13, 2019): 3951. http://dx.doi.org/10.1182/blood-2019-123052.
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Care of infants (<1 year of age) diagnosed with MLL (KMT2A)-rearranged acute lymphoblastic leukemia (MLLr-iALL) suffers from two major drawbacks. First, a poor survival rate due to a high rates of early relapse and chemo-resistance. Additionally, the approximately 30-50% of patients that do survive, suffer from life-long, debilitating side-effects of current treatment. While almost all MLLr-iALL patients show an initial promising response to treatment, two-third of the patients relapse, typically within the first year from diagnosis and while still on treatment. Accurate relapse prediction would allow implementation of treatment strategies that take relapse risk into account, with great potential benefit for all patients. Here, we show that Single-cell RNA sequencing (scRNA-seq) can be valuable for risk stratification and that the abundance of chemo-resistant cells within the diagnosis sample might be a powerful indicator of the likelihood of relapse. We have used scRNA-seq to analyze the response to treatment of leukemic cells in bone marrow biopsies of seven MLLr-iALL patients, expressing either the oncogenic MLL-AF4 or MLL-ENL fusion gene, at the time of initial diagnosis. Three of these patients successfully underwent treatment and remained disease-free during 7 years of follow-up, while in the remaining four cases the disease returned within a year from diagnosis. All samples were subjected to scRNA-seq by FACS index sorting with the aim of identifying differences between early relapsers and long-term survivors. Quantification of the proportion of cells classified by single cell transcriptomics, categorized as either chemo-resistant or chemo-sensitive, accurately predicts the occurrence of future relapse in individual patients. Strikingly, the single cell-based classification is even consistent with the order of relapse timing. Additionally, leukemic cells associated with high relapse risk are typified by a small phenotype, which coincides with an apparent quiescent gene expression pattern. This study clearly and, to the best of our knowledge, for the first time shows how disease classification and treatment management can directly benefit from single cell genomics. It demonstrates how classification based on a pivotal functional characteristic of single cells can be performed, despite individual patient variation. Our results shed light on the subpopulation from which leukemic relapse originates, and opens up opportunities for strong, risk-based strategies for future MLLr-iALL treatment regimens. Disclosures Pieters: medac: Consultancy; jazz farmaceuticals: Consultancy.
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Helal, Moutaz, Mara John, Emilia Stanojkovska, Greta Mattavelli, Lars Grundheber, Alexander Leipold, Nazia Afrin, et al. "Spatial Transcriptomics Reveals a Multi Cellular Ecosystem in Extramedullary Multiple Myeloma." Blood 140, Supplement 1 (November 15, 2022): 7060–61. http://dx.doi.org/10.1182/blood-2022-168700.
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Van Loon, Elisabet, and Maarten Naesens. "Blood transcriptomics as non-invasive marker for kidney transplant rejection." Néphrologie & Thérapeutique 17 (April 2021): S78—S82. http://dx.doi.org/10.1016/j.nephro.2020.02.012.
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SURYADEVARA, RAHUL, ANDREW GREGORY, ARIA MASOOMI, ZHONGHUI XU, SETH BERMAN, JEONG YUN, AABIDA SAFERALI, et al. "BLOOD TRANSCRIPTOMICS-BASED MACHINE LEARNING PREDICTION OF EMPHYSEMA IN SMOKERS." Chest 160, no. 4 (October 2021): A1841—A1842. http://dx.doi.org/10.1016/j.chest.2021.07.1653.
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Rosenbaum, James T., Christina A. Harrington, Robert P. Searles, Suzanne S. Fei, Amr Zaki, Sruthi Arepalli, Michael A. Paley, et al. "Revising the Diagnosis of Idiopathic Uveitis by Peripheral Blood Transcriptomics." American Journal of Ophthalmology 222 (February 2021): 15–23. http://dx.doi.org/10.1016/j.ajo.2020.09.012.
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Nahum, Laila A., Marina M. Mourão, and Guilherme Oliveira. "New Frontiers inSchistosomaGenomics and Transcriptomics." Journal of Parasitology Research 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/849132.
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Schistosomes are digenean blood flukes of aves and mammals comprising 23 species. Some species are causative agents of human schistosomiasis, the second major neglected disease affecting over 230 million people worldwide. Modern technologies including the sequencing and characterization of nucleic acids and proteins have allowed large-scale analyses of parasites and hosts, opening new frontiers in biological research with potential biomedical and biotechnological applications. Nuclear genomes of the three most socioeconomically important species (S. haematobium,S. japonicum, andS. mansoni) have been sequenced and are under intense investigation. Mitochondrial genomes of sixSchistosomaspecies have also been completely sequenced and analysed from an evolutionary perspective. Furthermore, DNA barcoding of mitochondrial sequences is used for biodiversity assessment of schistosomes. Despite the efforts in the characterization ofSchistosomagenomes and transcriptomes, many questions regarding the biology and evolution of this important taxon remain unanswered. This paper aims to discuss some advances in the schistosome research with emphasis on genomics and transcriptomics. It also aims to discuss the main challenges of the current research and to point out some future directions in schistosome studies.
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Zhu, Caiying, Yu Lian, Chenchen Wang, Peng Wu, Xuan Li, Yan Gao, Sibin Fan, et al. "Single-cell transcriptomics dissects hematopoietic cell destruction and T-cell engagement in aplastic anemia." Blood 138, no. 1 (March 24, 2021): 23–33. http://dx.doi.org/10.1182/blood.2020008966.
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Abstract Aplastic anemia (AA) is a T cell–mediated autoimmune disorder of the hematopoietic system manifested by severe depletion of the hematopoietic stem and progenitor cells (HSPCs). Nonetheless, our understanding of the complex relationship between HSPCs and T cells is still obscure, mainly limited by techniques and the sparsity of HSPCs in the context of bone marrow failure. Here we performed single-cell transcriptome analysis of residual HSPCs and T cells to identify the molecular players from patients with AA. We observed that residual HSPCs in AA exhibited lineage-specific alterations in gene expression and transcriptional regulatory networks, indicating a selective disruption of distinct lineage-committed progenitor pools. In particular, HSPCs displayed frequently altered alternative splicing events and skewed patterns of polyadenylation in transcripts related to DNA damage and repair, suggesting a likely role in AA progression to myelodysplastic syndromes. We further identified cell type–specific ligand-receptor interactions as potential mediators for ongoing HSPCs destruction by T cells. By tracking patients after immunosuppressive therapy (IST), we showed that hematopoiesis remission was incomplete accompanied by IST insensitive interactions between HSPCs and T cells as well as sustained abnormal transcription state. These data collectively constitute the transcriptomic landscape of disrupted hematopoiesis in AA at single-cell resolution, providing new insights into the molecular interactions of engaged T cells with residual HSPCs and render novel therapeutic opportunities for AA.
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Huang, Benjamin J., Jenny L. Smith, Timothy I. Shaw, Scott N. Furlan, Rhonda E. Ries, Amanda R. Leonti, Erin Lynn Crowgey, et al. "Integrated Transcriptomics and Proteomics Identifies Therapeutic Targets in Pediatric Acute Myeloid Leukemia." Blood 138, Supplement 1 (November 5, 2021): 1296. http://dx.doi.org/10.1182/blood-2021-153970.
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Abstract Acute myeloid leukemia (AML) remains a therapeutic challenge with high mortality rates despite intensive and myeloablative therapies. While immunotherapies targeting CD19 have yielded remarkable outcomes in acute lymphoblastic leukemia, identifying similar antigen therapeutic targets in AML remains a challenge due to inherent heterogeneity associated with AML and overlapping immunophenotypes with normal hematopoietic stem and myeloid cell populations. Transcriptional heterogeneity within pediatric AML has primarily been linked to underlying fusion. Therefore, we integrated large transcriptomics and proteomics datasets from AML and normal tissues to identify potential targets expressed in leukemias, but not in normal bone marrow or other normal tissue types. To identify candidate therapeutic targets in pediatric AML, we leveraged transcriptome sequencing data from bone marrow aspirates or peripheral blood collected from 1,481 children, adolescents, and young adults with AML at the time of diagnosis. Patients were enrolled on one of four Children's Oncology Group trials spanning the past three decades: CCG-2961, AAML03P1, AAML0531, and AAML1031. We also leveraged transcriptome sequencing from normal bone marrow (NBM) and normal CD34+ hematopoietic stem and progenitor cells (HSPCs) in order to exclude targets that are highly expressed during normal hematopoiesis. Finally, we performed additional filtering based on proteomic databases to exclude targets that lack membrane localization (Human Protein Atlas, UniProt, and Ensembl) or that are highly expressed on normal tissue types (Human Proteome Map, Human Protein Atlas, and Proteomics DB databases) (Figure 1A). First, we computed the log expression ratio between AMLs and NBM/HSCPs for all protein coding genes. We next selected genes expressed greater than a threshold of two standard deviations above the mean in 50% or more of AMLs (Figure 1B). Additionally, we further selected genes on the basis of differential gene expression and absolute expression thresholds. This analysis was repeated for our entire pediatric AML cohort and the following AML subtypes: RUNX1-RUNX1T1, CBFB-MYH11, KMT2A-MLLT3, KMT2A-MLLT10, KMT2A-MLLT4, KMT2A-ELL, KMT2A-MLLT1, KMT2A-MLLT11, NUP98-NSD1, NUP98-KDM5A, and CBFA2T3-GLIS2. Candidate therapeutic targets were filtered based on membrane localization and normal tissue expression using the aforementioned proteomics databases (Figure 1C and 1D). Based on this algorithm, we identified a nonzero number of candidate therapeutic targets for each of our pediatric AML subtypes (Figure 1D). Intriguingly, we found no overlap between targets identified in our pediatric, adolescent, and young adult cohort and a previous similar analysis performed in AMLs diagnosed in older patients (PMID 29017060). This study demonstrates that by combining our large transcriptomics dataset with pre-existing proteomics datasets, we are able to identify a collection of candidate therapeutic targets in pediatric AML. Importantly, zero targets were identified that were inclusive to all pediatric AMLs within our cohort, which underscores the transcriptional heterogeneity that our group and others have previously identified in pediatric AML. Future preclinical and clinical studies will need to account for this heterogeneity by prioritizing targets on the basis of underlying molecular alteration. Our study comprises a platform and dataset of candidate targets for further functional validation and/or immunotherapy targeting studies in pediatric AML. Figure 1 Figure 1. Disclosures Shaw: T-Cell and/or Gene Therapy for Cancer: Patents & Royalties.
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Dong, Ruochen, Jonathon Russell, Hua Li, Seth Malloy, Kate Hall, Kaitlyn Petentler, Allison Scott, et al. "Using Spatial Transcriptomics to Reveal Fetal Liver Hematopoietic Stem Cell-Niche Interactions." Blood 140, Supplement 1 (November 15, 2022): 1688–89. http://dx.doi.org/10.1182/blood-2022-170352.
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Mao, Xinjian, Ning Zhang, Kate Hall, Kaitlyn Petentler, Allison Scott, Seth Malloy, Fang Liu, et al. "Characterization of Dynamic Niche Signaling Modules during Embryonic Hematopoietic Development Using Spatial Transcriptomics." Blood 140, Supplement 1 (November 15, 2022): 8568–69. http://dx.doi.org/10.1182/blood-2022-160325.
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Granata, Simona, Alessandra Dalla Gassa, Gloria Bellin, Antonio Lupo, and Gianluigi Zaza. "Transcriptomics: A Step behind the Comprehension of the Polygenic Influence on Oxidative Stress, Immune Deregulation, and Mitochondrial Dysfunction in Chronic Kidney Disease." BioMed Research International 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/9290857.
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Chronic kidney disease (CKD) is an increasing and global health problem with a great economic burden for healthcare system. Therefore to slow down the progression of this condition is a main objective in nephrology. It has been extensively reported that microinflammation, immune system deregulation, and oxidative stress contribute to CKD progression. Additionally, dialysis worsens this clinical condition because of the contact of blood with bioincompatible dialytic devices. Numerous studies have shown the close link between immune system impairment and CKD but most have been performed using classical biomolecular strategies. These methodologies are limited in their ability to discover new elements and enable measuring the simultaneous influence of multiple factors. The “omics” techniques could overcome these gaps. For example, transcriptomics has revealed that mitochondria and inflammasome have a role in pathogenesis of CKD and are pivotal elements in the cellular alterations leading to systemic complications. We believe that a larger employment of this technique, together with other “omics” methodologies, could help clinicians to obtain new pathogenetic insights, novel diagnostic biomarkers, and therapeutic targets. Finally, transcriptomics could allow clinicians to personalize therapeutic strategies according to individual genetic background (nutrigenomic and pharmacogenomic). In this review, we analyzed the available transcriptomic studies involving CKD patients.
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Li, Yuping, Xiaoqian Liu, Xuxiang Liu, Xiwei Wu, Alyssa Bouska, Waseem G. Lone, Chan-Wang J. Lio, et al. "Single-Cell Transcriptomics of Human TET2 Knockout CD4 T-Cells and Their Clonal Evolution." Blood 136, Supplement 1 (November 5, 2020): 22–23. http://dx.doi.org/10.1182/blood-2020-143113.
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Angioimmunoblastic T-cell lymphoma (AITL), the most frequent subtype of peripheral T-cell lymphoma (PTCL), is a neoplasm with characteristics of mature T follicular helper (TFH) cells. We and others have identified frequent (~75%) inactivating mutations in the TET2 (Ten-Eleven Translocation-2) gene in AITL. TET2 belongs to a 3 member family of TET dioxygenases that catalyze DNA demethylation by oxidation of 5-methyl-cytosine (5-mC) to 5-hydroxymethyl-cytosine (5-hmC) and further oxidative cytosine products. Thus, loss of function (LOF) of TET2 will cause aberrant genome hypermethylation and reduction in 5-hmC. Studies of the variant allele fraction (VAF) of TET2 mutants suggest that this mutation is a founding abnormality in AITL. However, how TET2 loss promotes the development of AITL is still unclear. To study LOF of TET2 in CD4 T-cell lymphomagenesis without the noise generated by other mutations in an established lymphoma, we generated a human TET2 knock-out (KO) CD4 T-cell model using CRISPR/Cas9 technology, which allows us to perform functional genomic studies by directly editing genes at their genomic loci. Whole transcriptome sequencing and single-cell transcriptome sequencing were used to study the cell evolution after KO. We generated multiple TET2 KO primary CD4 T-cell models using two different CRISPR/Cas9 methods. The first approach used the plasmid PX458-a, which expresses green fluorescent protein (GFP) fused Cas9 and guide RNA-a targeting TET2 exon 6, to electroporate CD4 T-cell from healthy donor F25. The second approach used homologous DNA repair (HDR) mediated knock-in (KI) of tandem GFP gene and a SV40 transcription stop signal to terminate TET2 expression at exon 3. Cas9/sgRNA-e RNP complex, along with a long DNA template (about 1.6 kb), was electroporated into CD4 T-cells from two healthy donors, F25 and M40. GFP-positive cells were sorted by FACS after electroporation and were considered to be edited cells. Edited CD4 T-cells were cultured in vitro with 50 U/ml IL-2, and stimulated regularly (every 7~10 days) with 1:1 ratio of anti-CD3/CD28 T activator beads. TET2 KO in these cells was confirmed by qRT-PCR, Sanger sequencing and Western blotting. Compared with wild-type (WT) CD4 T-cells under the same culture conditions, a lower level of 5-hmC in TET2 KO cells was observed, indicating successful editing of TET2. Compared to WT cells, KO cells had a higher growth rate, due to a lower apoptosis rate and a higher proliferation rate, by Annexin V staining, EdU staining, and MTS experiments. The growth of KO cells or WT cells was still dependent on IL-2 and T activator beads stimulation. All batches of KO cells, generated by different guide RNAs or from different donors, showed a much longer life span than WT cells, which usually lived for 3~4 months, but KO cells can keep proliferating longer than one year. We also performed TCR analysis on these cell samples. Both WT and KO cells demonstrated oligoclonality when examined at Day 40 (40D, early stage) and TET2 KO cells showed a dominant clone by Day 90 (90D, late stage). We performed single-cell transcriptome analysis on M40 KO vs. WT cells, at 40D and 90D. KO90D cells had a low TCR diversity with the dominant population representing ~88% of cells (TRAV9-2,TRBV5-1). From single-cell transcriptome analysis, cell clustering profiles were very distinctive in these 4 cell populations analyzed (Figure 1A) and these clusters had unique gene expression profiles (Figure 1B). Cluster 6 was prominent in KO90D but almost absent in WT90D, whereas the reverse was true for clusters 1 and 5. From pathway analysis, KO90D cells showed a higher expression of signatures associated with proliferation, cell cycle and chemokine signaling and lower histidine and tryptophan metabolism signatures. Sanger sequencing showed a 79 bp indel in addition to the GFP KI allele in KO90D cells, demonstrated the homozygous deletion of TET2 on these cells. Similar results were observed in F25 TET2 KO cells by plasmid PX458-a. This indicated the selection of homozygously deleted TET2 cells in long-term culture. However, clonal evolution is highly dynamic and a minor clone in KO40D cells may become the dominant clone in KO90D cells. Comparison of the 5-mC and 5-hmC profiles between KO and WT cells are being conducted to elucidate epigenetic alterations that are associated with the functional alterations and predisposition to AITL lymphomagenesis. Figure Disclosures No relevant conflicts of interest to declare.
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Sierra, Beatriz, Ana Cristina Magalhães, Daniel Soares, Bruno Cavadas, Ana B. Perez, Mayling Alvarez, Eglis Aguirre, Claudia Bracho, Luisa Pereira, and Maria G. Guzman. "Multi-Tissue Transcriptomic-Informed In Silico Investigation of Drugs for the Treatment of Dengue Fever Disease." Viruses 13, no. 8 (August 4, 2021): 1540. http://dx.doi.org/10.3390/v13081540.
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Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico–informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, “Adrenergic receptor antagonist”, “ATPase inhibitor”, “NF-kB pathway inhibitor” and “Serotonin receptor antagonist”, were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.
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Abdullah, Mohammad Nasir, Yap Bee Wah, Abu Bakar Abdul Majeed, Yuslina Zakaria, and Norshahida Shaadan. "Identification of Blood-Based Multi-Omics Biomarkers for Alzheimer’s Disease Using Firth’s Logistic Regression." Pertanika Journal of Science and Technology 30, no. 2 (March 14, 2022): 1197–218. http://dx.doi.org/10.47836/pjst.30.2.19.
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Alzheimer’s disease (AD) is a progressive and relentless debilitating neurodegenerative disease. A post-mortem microscopic neuropathological examination of the brain revealed the existence of extracellular β-amyloid plaques and intracellular neurofibrillary tangles. An accurate early diagnosis of AD is difficult because various disorders share the initial symptoms of the disease. Based on system biology, the multi-omics approach captures and integrates information from genomics, transcriptomics, proteomics, cytokinomics, and metabolomics. This study developed an AD prediction model based on the integrated blood-based multi-omics dataset involving 32 AD patients and 15 non-AD subjects. The integrated multi-omics dataset consists of 16 transcript genes, 14 metabolites, and nine cytokines. Due to the complete separation and multicollinearity issues, Firth’s logistic regression model was then developed to predict AD using the principal components. The model revealed 18 potential biomarkers of AD, consisting of seven metabolites, two transcriptomes, and nine cytokines. These potential biomarkers show an upregulated risk in the AD group compared to the non-AD subjects. The possibility of using these biomarkers as early predictors of AD is discussed.
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Sellamuthu, Rajendran, Christina Umbright, Jenny R. Roberts, Rebecca Chapman, Shih-Houng Young, Diana Richardson, Jared Cumpston, et al. "Transcriptomics analysis of lungs and peripheral blood of crystalline silica-exposed rats." Inhalation Toxicology 24, no. 9 (August 2012): 570–79. http://dx.doi.org/10.3109/08958378.2012.697926.
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Hadland, Brandon, Barbara Varnum-Finney, Stacey Dozono, Tessa Dignum, Cynthia Nourigat-Mckay, Dana Jackson, Shahin Rafii, Cole Trapnell, and Irwin D. Bernstein. "Integrated Single Cell Transcriptomics Defines an Engineered Niche Supporting Hematopoietic Stem Cell Development Ex Vivo." Blood 134, Supplement_1 (November 13, 2019): 3699. http://dx.doi.org/10.1182/blood-2019-126109.
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During embryonic development, hematopoietic stem cells (HSC) arise from hemogenic endothelial cells (HEC) within arterial vessels such as the aorta of the AGM (aorta-gonad-mesonephros) region, in a process referred to as the endothelial to hematopoietic transition (EHT). Although numerous signal pathways have been implicated in EHT, the precise combination of niche-derived signals required to support the generation and self-renewal of functional, long-term engrafting HSC remains poorly defined. To elucidate the niche signals regulating HSC emergence, we used single cell RNA-sequencing to simultaneously analyze the global transcriptional profiles of HEC during their transition to HSC and the AGM-derived endothelial cell stroma (AGM-EC) that supports the generation and expansion of functional HSC. Trajectory analysis of single cell transcriptomes enabled reconstruction of EHT in pseudotime, revealing dynamics of gene expression, including genes encoding cell surface receptors and downstream pathways, during the process of HSC genesis and self-renewal in vivo and in vitro. Transcriptional profiles of niche AGM-EC enabled identification of corresponding ligands which serve to activate these receptors during HSC generation. We integrated this knowledge to engineer a stromal cell-free niche for generation of engrafting HSC from hemogenic precursors in vitro. Specifically, we defined serum-free conditions combining immobilized Notch1 and Notch2-specific antibodies to activate Notch receptors, recombinant VCAM1-Fc chimera or fibronectin fragment to bind VLA-4 integrin, recombinant interleukin-3, stem cell factor, thrombopoietin, and CXCL12 to activate their respective cytokine/chemokine receptors, and small molecule inhibition of TGF-β Receptor 1. We demonstrated that this engineered niche is sufficient to support the generation of functional HSC, as measured by long-term (24 week) multilineage engraftment after transplantation to immune-competent, lethally irradiated adult recipient mice, following culture of hemogenic precursors isolated from E9.5 to E10.5 murine embryos. The observed efficiency of generating long-term engrafting HSC, particularly from precursors derived from early embryonic stages before E10, was lower in engineered conditions compared with AGM-EC stroma, suggesting additional niche signal factors remain to be defined to optimally support HSC maturation and self-renewal in the engineered niche. Single cell RNA-sequencing of hematopoietic progeny generated following culture in the engineered niche demonstrated the formation of populations with transcriptional signatures of HSC, as well as multipotent and lineage-specific progenitors, comparable to those generated following co-culture with niche AGM-EC stroma. However, we observed relative overexpression of Notch target genes promoting early T-lymphoid fate in cells generated from the engineered niche compared to those from AGM-EC stroma. Incorporating stage-specific attenuation of Notch1 receptor activation with soluble Notch1 blocking antibody during culture was sufficient to limit markers of early T-cell precursors, suggesting that temporal titration of Notch signal activation could be used to further modulate HSC and T-lymphoid output in the engineered niche. Altogether, these studies enhance our understanding of the core signal pathways necessary for the embryonic development of functional HSC, with the potential to advance in vitro engineering of therapeutically relevant pluripotent stem cell-derived HSC in stromal cell-free culture. Disclosures Bernstein: Lyell Immunopharma: Consultancy, Equity Ownership, Patents & Royalties, Research Funding; Nohla Therapeutics: Consultancy, Equity Ownership, Patents & Royalties, Research Funding.
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Haas, Simon, Chiara Baccin, Jude Al-Sabah, Lars Velten, Steinmetz Lars, and Andreas Trumpp. "Combined Single-Cell and Spatial Transcriptomics to Deconvolute the Hematopoietic Stem Cell Niche." Blood 132, Supplement 1 (November 29, 2018): 876. http://dx.doi.org/10.1182/blood-2018-99-118479.
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Abstract Coordinated interaction of many cell types is required to facilitate hematopoietic and mesenchymal stem cell maintenance and differentiation in the bone marrow. However, the molecular factors and cell types involved in this complex interplay remain poorly understood. Here we developed a combined single cell and spatial transcriptomics approach to address this problem. Large-scale single-cell transcriptional profiling in conjunction with a multi-layered sorting approach allowed us to generate a complete and evenly sampled transcriptional map of all major bone and bone marrow populations. Our dataset covers all cell types or differentiation trajectories involved in mesenchymal and hematopoietic stem cell differentiation, osteogenesis, adipogenesis, myelopoiesis, erythropoiesis, lymphopoiesis, memory T cell formation as well as bone marrow neural innervation and vascularization at the single cell level. Using this data, we derive fundamental properties of the described cell types, clarify the cellular source of signals affecting stem cell differentiation processes and provide a systems view on putative intercellular interactions. Systematic spatial transcriptomics, using laser-capture microdissection of selected bone marrow niches followed by transcriptional profiling and bioinformatic cellular deconvolution, allowed us to confirm predicted interactions and map the cellular composition of distinct bone marrow niches. Our analyses highlight the importance of pre-adipogenic CXCL12 abundant reticular cells as key niche cells for stem cell maintenance, provides a holistic systems view of the hematopoietic stem cell niche and offers a novel approach to systematically deconvolute the molecular, cellular and spatial composition of complex tissues. Disclosures No relevant conflicts of interest to declare.
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Kiechle, Frederick L., and Carol A. Holland-Staley. "Genomics, Transcriptomics, Proteomics, and Numbers." Archives of Pathology & Laboratory Medicine 127, no. 9 (September 1, 2003): 1089–97. http://dx.doi.org/10.5858/2003-127-1089-gtpan.
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Abstract Objective.—To review the advances in clinically useful molecular biologic techniques and to identify their applications in clinical practice, as presented at the 11th Annual William Beaumont Hospital DNA Symposium. Data Sources.—The 8 manuscripts submitted were reviewed, and their major findings were compared with literature on the same or related topics. Study Selection.—Manuscripts address the use of molecular techniques in microbiology to evaluate infectious disease and epidemiology; molecular microbiology methods, including rapid-cycle real-time polymerase chain reaction; peroxisome proliferator–activated receptor γ as a potential therapeutic target in inflammatory bowel disease or colon cancer; the effect of nonapoptotic doses of the bisbenizamide dye Hoechst 33342 on luciferase expression in plasmid-transfected BC3H-1 myocytes; the routine use of cystic fibrosis screening and its challenges; and the use of flow cytometry and/or chromosomal translocation in the diagnostic evaluation of hematopoietic malignancies. Data Synthesis.—Three current issues related to the use of molecular tests in clinical laboratories are (1) the restriction on introducing new tests secondary to existing patents or licenses; (2) the preanalytic variables for the different specimen types currently in use, including whole blood, plasma, serum, fresh or frozen tissues, and free-circulating DNA; and (3) the interpretation of studies evaluating the association of complex diseases with a single mutation or single-nucleotide polymorphism. Molecular methods have had a major impact on infectious disease through the rapid identification of organisms, the evaluation of outbreaks, and the characterization of drug resistance when compared with standard culture techniques. The activation of peroxisome proliferator–activated receptor γ stimulated by thiazolidinedione is useful in the treatment of type II diabetes mellitus and may have value in preventing inflammatory bowel disease or colon cancer. Hoechst 33342 binding to adenine-thymine–rich regions in the minor groove of DNA is a fluorescent stain for DNA and initiates apoptosis at >10 μg/mL. Lower doses of Hoechst 33342 promote luciferase expression by a mechanism that may involve binding to cryptic promoters facilitated by dye-associated misalignment of the tertiary structure of DNA. The routine use of cystic fibrosis screening is complicated by the more than 1000 mutations associated with the disease. The use of 4-color flow cytometry and the detection of chromosomal translocation are both invaluable aids in establishing the diagnosis of lymphoid or myeloid hematopoietic malignancies. Conclusions.—The current postgenomic era will continue to emphasize the use of microarrays and database software for genomic, transcriptomic, and proteomic screening in the search for useful clinical assays. The number of molecular pathologic techniques will expand as additional disease-associated mutations are defined.
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Gregory, Andrew, Zhonghui Xu, Katherine Pratte, Seth Berman, Robin Lu, Rahul Suryadevara, Robert Chase, et al. "Blood RNA and protein biomarkers are associated with vaping and dual use, and prospective health outcomes." F1000Research 12 (February 2, 2023): 123. http://dx.doi.org/10.12688/f1000research.128583.1.
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Background: Electronic nicotine delivery systems (ENDS) are driving an epidemic of vaping. Identifying biomarkers of vaping and dual use (concurrent vaping and smoking) will facilitate studies of the health effects of vaping. To identify putative biomarkers of vaping and dual use, we performed association analysis in an observational cohort of 3,892 COPDGene study participants with blood transcriptomics and/or plasma proteomics data and self-reported current vaping and smoking behavior. Methods: Biomarkers of vaping and dual use were identified through differential expression analysis and related to prospective health events over six years of follow-up. To assess the predictive accuracy of multi-biomarker panels, we constructed predictive models for vaping and smoking categories and prospective health outcomes. Results: We identified three transcriptomic and three proteomic associations with vaping, and 90 transcriptomic and 100 proteomic associations to dual use. Many of these vaping or dual use biomarkers were significantly associated with prospective health outcomes, such as FEV1 decline (three transcripts and 62 proteins), overall mortality (18 transcripts and 73 proteins), respiratory mortality (two transcripts and 23 proteins), respiratory exacerbations (13 proteins) and incident cardiovascular disease (24 proteins). Multimarker models showed good performance discriminating between vaping and smoking behavior and produced informative, modestly powerful predictions of future FEV1 decline, mortality, and respiratory exacerbations. Conclusions: In summary, vaping and dual use are associated with RNA and protein blood-based biomarkers that are also associated with adverse health outcomes.
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Kanaan, Sami B., Shruti Bhise, Todd M. Cooper, Soheil Meshinchi, and Scott N. Furlan. "Single-Cell Transcriptomics for Residual Disease Detection in Acute Myelogenous Leukemia Post Allogeneic Hematopoietic Cell Transplantation." Blood 138, Supplement 1 (November 5, 2021): 518. http://dx.doi.org/10.1182/blood-2021-151504.
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Abstract Detection of residual disease is a critical component of modern, risk-adapted therapy for Acute Myeloid Leukemia (AML). However, the genetic and phenotypic diversity of AML has made the development of a universal assay for disease assessment particularly challenging. While purely mutation-based tests promise high sensitivity, they are not broadly applicable given molecular heterogeneity and complex clonal evolution. Single-cell approaches, such as multiparameter flow cytometry (MFC), are more broadly applicable and increasingly accepted as the standard in clinical care. However, the limited number of leukemia-specific cell-surface markers and high numbers of shared markers between malignant myeloid blasts and healthy progenitors make MFC data extremely challenging to interpret. Motivated to develop a broadly applicable assay that can provide a more confident assessment of residual disease, we developed a platform using droplet-partitioned single-cell RNA sequencing accompanied by a computational pipeline specifically tailored to quantify residual disease after allogeneic HCT (alloHCT). With bone marrow samples from an 11-year-old patient with suspected post-alloHCT relapse of AML, we interrogated three methods of sample processing, 1) RBC lysis, 2) Ficoll-centrifugation, and 3) Ficoll-centrifugation combined with CD34+ immunomagnetic selection. The samples were further split to separately capture the 3' or 5' end of polyadenylated transcripts. The six resulting libraries were sequenced using standard short-read sequencing, and reads were demultiplexed and counted using common workflows. Data from the samples were combined, and sub-populations were visualized using UMAP (see Figure). This study demonstrated the feasibility of real-time single-cell sequencing for clinical utility. It is possible to process, capture, and sequence a patient's sample in approximately three working days (A). By integrating our data with single-cell expression profiles from an atlas of healthy human bone marrow, we were able to identify cells with gene-expression programs distinct from those of normal hematopoietic cells (B). With these integrated data, we could clearly identify populations of cells that embed away from healthy atlas cells (yellow circle, B), defining a different than normal single-cell profile. This "malignant" profile also included several genes whose expression is usually restricted to healthy hematopoietic progenitors (Panel C), suggesting these cells had a severely dysregulated transcriptome. As this patient was post-alloHCT, we interrogated the abundance of single-nucleotide-polymorphisms (SNPs) in the sequence data. We quantified these SNPs in single cells to distinguish each cell as either of donor or recipient origin using a method we have previously validated for genotyping RNA sequence in single cells. We clearly demonstrate that those cells identified as "different than normal" have a distinct SNP profile suggesting they are of recipient origin. Further analysis revealed that this malignant population was highly enriched for a population of cells expressing a previously described set of "AML-restricted genes" (Huang, B. et al., ASH 2021). (Panel E). Finally, from the Ficoll-processed sample, we quantified a level of 9.8% residual disease (243 malignant cells from a total of 2487). Notably, the number of abnormal myeloid progenitors determined by MFC was 2.0% which increased to 13% on a subsequent marrow sample drawn one week later. Incidentally, we observed only minimal differences across the two single-cell sequencing chemistries (3' vs. 5'). Taken together, our data strongly argue that droplet-based, single-cell RNA sequencing is a feasible and powerful tool for the ascertainment of residual disease in AML. Given the robust nature of the platform and the ability to incorporate SNP integration into the analytic pipeline, it allows confident detection of residual disease in the post-alloHCT setting. By combining genomic quantification of transcripts with the power of SNP-based genotyping all at the level of the single cells, we believe this technology can substantially improve our diagnosis of post-alloHCT AML relapse. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Ainciburu, Marina, Teresa Ezponda, Nerea Berastegui, Ana Alfonso Pierola, Amaia Vilas-Zornoza, Patxi San Martin-Uriz, Diego Alignani, et al. "Single-Cell Transcriptomics Study of Human Hematopoietic Progenitors Reveals Alterations Associated with Aging and Myeloid Malignancies." Blood 138, Supplement 1 (November 5, 2021): 1082. http://dx.doi.org/10.1182/blood-2021-153550.
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Abstract Hematopoietic stem and progenitor cells (HSPCs) comprise a continuum of cells with varying differentiation potential and priming toward specific lineages. During both healthy aging and myeloid malignancies, changes occur in the composition and regulation of HSPCs. In this study, we evaluated human HSPCs obtained from young and elderly healthy donors using single-cell RNA sequencing to identify the transcriptional and regulatory alterations associated with aging at single cell resolution. We then applied this knowledge to the study of specific perturbations associated with the development of myeloid pathologies. We isolated >90,000 bone marrow CD34+ cells from 5 young (18-20 y/o), 3 elderly (>65 y/o) healthy donors, 1 patient with myelodysplastic syndrome (MDS) and 1 patient with acute myeloid leukemia (AML), using fluorescence-activated cell sorting. scRNA libraries were prepared with the 10X chromium platform and sequenced. Finally, bioinformatic analysis was performed using available R and Python algorithms such as Seurat, Palantir and Scenic. First, we characterized HSPC subpopulations in young donors by unsupervised clustering and manual annotation. Taking the previous findings as reference, we then classified the elderly and pathological HSPC using elastic-net regularization prediction models (Figure 1A). Comparison of subpopulations in young and elderly donors confirmed the age-related increase in HSC, as well as reduction of lymphoid progenitors and myelomonocytic compartments. Next, we performed differential expression and pathways analysis to uncover age-associated alterations in the transcriptional profile of cells with the same identity. We found a generalized enrichment in elderly HSPC of pathways activated upon stress and inflammation, such as p53, hypoxia and TNF alpha response. This suggests an age-related increased response to the more inflammatory microenvironment of elderly individuals. On the other hand, young HSPC were enriched for cell cycle activation and proliferation pathways, as well as metabolic processes (Figure 1B). Using trajectory analysis, we recovered 6 differentiation paths present in our young donor's data. When compared to the elderly, the greatest changes occurred along the monocytic trajectory. For some genes, expression differed through the whole trajectory, indicating the existence of original transcriptional alterations already at the HSC compartment. On the other hand, expression of myelomonocytic differentiation markers, such as MPO and CD74, reached lower levels in our elderly HSPC data, pointing towards a loss of capacity for monocytic differentiation in progenitors from elderly individuals. Finally, to identify key transcription factors regulating the progression of differentiation routes, we built gene regulatory networks. Overall, we found lower activation levels for transcriptional programs in the early progenitors from elderly donors. In addition, gene ontology enrichment analysis showed that the active networks in the young were enriched for differentiation-related terms, while networks from the elderly were not. These results also indicate an age-associated loss of differentiation capability. We then applied the same computational tools to analyze aberrant hematopoiesis in samples from 2 patients suffering from myeloid malignancies (MDS and AML). On one hand, we subjected the MDS sample to trajectory analysis, focusing on the erythroid lineage. We observed perturbations in the expression dynamics of genes playing a role in erythropoiesis. In the AML sample, we encountered a significant expansion of the most immature cell compartments (HSC, LMPP and MEP). In addition, GRN reconstruction showed up the specific activity of transcription programs activated by factors deregulated during leukemia, such as ZSCAN18 and GFI1. In conclusion, our work described the transcriptional alterations that occur in early hematopoiesis, both during healthy aging and myeloid pathology. We used multiple approaches, such as the study cellular proportions, differentiation trajectories and GRNs. The inclusion of samples from patients with myeloid pathology provided insights into the potential role of single-cell technologies for understanding and treating hematological malignancies. Figure 1 Figure 1. Disclosures Sanchez-Guijo: Gilead: Consultancy, Honoraria; Celgene/Bristol-Myers-Squibb,: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Takeda: Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding. Diez-Campelo: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Valcarcel: BMS: Consultancy, Honoraria, Speakers Bureau; CELGENE: Consultancy, Honoraria, Speakers Bureau; ASTELLAS: Consultancy, Honoraria, Speakers Bureau; AMGEN: Consultancy, Honoraria, Speakers Bureau; NOVARTIS: Consultancy, Honoraria, Speakers Bureau; TAKEDA: Consultancy, Honoraria, Speakers Bureau; JAZZ: Consultancy, Honoraria, Speakers Bureau; SOBI: Consultancy, Honoraria, Speakers Bureau; SANOFI: Consultancy, Honoraria, Speakers Bureau. Romero: 10X Genomics: Current Employment. Prosper: Janssen: Honoraria; Oryzon: Honoraria; BMS-Celgene: Honoraria, Research Funding.
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Haase, Christa, Karin Gustafsson, Shenglin Mei, Jelena Milosevic, Shu-Chi Yeh, David Sykes, Peter Kharchenko, David T. Scadden, and Charles Lin. "Spatial Transcriptomics Reveals DPP4 As Novel Marker of a More Proliferative Phenotype in Early AML Progression." Blood 138, Supplement 1 (November 5, 2021): 3310. http://dx.doi.org/10.1182/blood-2021-151917.
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Abstract Acute myeloid leukemia (AML) is a hematologic malignancy with poor prognosis for which the standard-of-care chemotherapy treatment regimen has remained virtually unchanged over the past 40 years. We have employed "Image-Seq", a new technology that was developed in our laboratory, to study spatial variations in early leukemia progression in a mouse model of HoxA9-Meis1 AML. We visualized leukemia cells with differing proliferative phenotype using intravital microscopy, captured these cells under image guidance from individual bone marrow microenvironments and studied their differential expression by single-cell RNA sequencing. This analysis identified DPP4 as a key upregulated gene in AML cells from more proliferative bone marrow compartments and associated DPP4 expression with a cell cluster enriched in progenitor cell markers for HoxA9-Meis1 AML, including Flt3, Itgb7 and Ddx4. Strikingly, DPP4 is not expressed in vitro, and its expression in vivo (as quantitated by FACS analysis) correlated with disease progression and marked a more proliferative phenotype both at the 1-week and 2-week time-points during disease progression. Disclosures Sykes: Clear Creek Bio: Current equity holder in publicly-traded company; SAFI Biosolutions: Consultancy, Current equity holder in publicly-traded company; Keros Therapeutics: Consultancy. Scadden: Magenta Therapeutics: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; VCanBio: Consultancy; LifeVaultBio: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Inzen Therapeutics: Membership on an entity's Board of Directors or advisory committees; Garuda Therapeutics: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; FOG Pharma: Consultancy; Fate Therapeutics: Current holder of individual stocks in a privately-held company; Editas Medicines: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Dainippon Sumitomo Pharma: Other: sponsored research; Clear Creek Bio: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees.
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Ennis, Sarah, Alessandra Conforte, Sukhraj Pal Singh Dhami, Philippe Krebs, Michael O'Dwyer, and Eva Szegezdi. "Single Cell Transcriptomics Revealed Molecular Alterations in AML Cell Clusters Relevant to Refractory Disease at Relapse." Blood 138, Supplement 1 (November 5, 2021): 3316. http://dx.doi.org/10.1182/blood-2021-153910.
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Abstract Introduction: Drug resistance at relapse is a major cause of mortality in AML. Previous genomic profiling of AML patient samples revealed that in many cases the mutational profile did not change between diagnosis and relapse (Parkin et al., Blood, 2013, Nuno K et al., Blood, 2020), indicating that epigenetic changes can have a substantial contribution to acquired drug resistance and refractory disease at relapse. In order to uncover such functional alterations, longitudinal AML patient samples collected at diagnosis, during remission and relapse were analysed with single cell transcriptomics (scRANseq) in order to identify how the molecular wiring of AML cells evolve during disease progression and what alterations make them drug resistant. Methods: scRNAseq was carried out (10x Genomics) on mononuclear cell fractions from bone marrow aspirates of 7 AML patients (from the Finnish Hematology Registry and Clinical Biobank) who received AraC-based induction therapy, achieved CR but later relapsed. Raw data was QC processed (CellRanger), batch corrected (scArches, Harmony). Cell types were identified as described by Granja et al., Nat. Biotechn., 2020 (Fig 1A), the myeloid cell lineage was selected, clusters identified (Leiden) and differentially expressed genes identified (EdgeR). For mechanistic studies primary AML blasts were cultured with bone marrow stromal cells (BMSC) in a system able to recapitulate in vivo chemotherapy resistance (Dhami et al., Br. J, Haematol, 2020). Results: The scRNAseq analysis generated transcriptome for over 67,000 myeloid-lineage cells (hematopoietic/leukemic stem cells (HSC/LSC), multipotent progenitors (MPP), granulocyte-monocyte progenitors (GMP), monocytes (CD14, CD16 subsets), conventional and plasmacytoid dendritic cells; c/pDCs) including 21K, 26K and 22K cells at diagnosis, remission and relapse, respectively (Fig 1B). To identify drug resistant AML clones, we integrated the myeloid lineage cells from the 7 patients and identified clusters (Fig 1C). Of the 24 clusters found, the ones rich in differentiated cells became abundant at remission, while clusters rich in low-differentiation status cells (HSC/LSC, MPP, GMP) diminished. Upon relapse however this trend reversed and all main clusters present at diagnosis returned, indicating that cells from all clusters survived chemotherapy or drug-resistant LSCs could reinstate the full disease spectrum (Fig 1D, 1E). Analysis of AML cell clusters in each patient individually confirmed this pattern with nearly all clusters returning at relapse, excluding the possibility that integration masked patient-level heterogeneity/patient-specific cluster patterns (Fig 1F). Despite the identical cluster distribution between diagnosis and relapse, all 7 patients showed refractory disease at relapse, indicating that small scale molecular alterations, which are not substantial enough to segregate the cells into a new cluster are sufficient to drive drug resistance. To identify these changes, genes differentially expressed and associated gene ontology (GO) terms between diagnosis and relapse were identified for each cluster. The analysis found GO terms commonly regulated in multiple clusters, most notably myeloid cell activation, mitochondrial ATP metabolism, cellular respiration, mRNA maturation and translation. In order to determine whether targeting the protein synthesis machinery, as a process linked to integrated stress response and HSC maintenance, can be exploited to eliminate drug resistant AML cells, we exposed BMSCs to transient proteostatic stress induced by the NEDDylation inhibitor pevonedistat (1 mM for 24 h), cultured primary AML blasts on the stress-conditioned BMSCs and exposed them to drug treatments (Fig 1G). Stress-conditioned BMSCs lost their ability to support AML blast survival and to protect them from tyrosine kinase inhibitors (quizartinib) and the combination of AraC+daunorubicin. Conclusions: We found that AML cell clusters that already exist at diagnosis re-emerge at relapse, although with re-occurring molecular alterations that can provide drug resistance resulting in refractory disease. Targeting one of these pathways, proteostasis, could break the protective interaction between BMSCs and AML blasts resulting in reduced AML survival and enhanced drug sensitivity, representing a potential actionable vulnerability of drug resistant AML cells. Figure 1 Figure 1. Disclosures Szegezdi: Bristol Myers Squibb: Research Funding; ONK Therapeutics: Research Funding.
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Bonolo De Campos, Cecilia, Caleb K. Stein, Nathalie Meurice, Laura Ann Bruins, Joachim L. Petit, Alysia N. Polito, Gregory J. Ahmann, et al. "Integrative Analysis of FISH, Transcriptomics and Mutational Status Predicts Responsiveness to Novel Agents in Multiple Myeloma." Blood 134, Supplement_1 (November 13, 2019): 574. http://dx.doi.org/10.1182/blood-2019-130977.
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Introduction Despite continuous improvement of clinical outcome in multiple myeloma (MM), disease relapse remains a major challenge, leading to progressively shorter remissions and fewer treatment options. Strategies attempting to counteract this challenge include recent efforts resulting in an increase in the availability of novel promising anti-MM agents and targeting specific genetic profiles of the disease. In this context, we aim to develop predictive models of sensitivity and resistance to novel compounds by connecting an ex vivo high-throughput drug screen with genetic, transcriptomics, FISH, and clinical features. Methods Twenty compounds (afatinib, afuresertib, belinostat, buparlisib, cobimetinib, CPI-0610, crenolanib, dinaciclib, dovitinib, JQ1, LGH447, osimertinib, OTX015, panobinostat, romidepsin, selinexor, sunitinib, trametinib, venetoclax, and vorinostat) were selected based on overall promising anti-MM activity from an ex vivo high throughput drug screen with a panel of 79 single agents incubated for 24 hours. The area under the curve (AUC) was used to rank order the ex vivo responses for each compound and the lowest and highest quartile samples were identified for further analysis. Clinical data and FISH data, including t(11;14), t(4;14), t(14;16), del(17p), +1q, monosomy 13, and MYC rearrangement, were collected. Targeted DNA sequencing was performed using a 2.3 Mb custom capture panel covering 139 MM-relevant genes. mRNA-sequencing was performed and differential gene expression analysis in the highest and lowest quartile identified subsets of markers positively and negatively associated with the AUC response for a given compound. An additional unbiased selection of markers using lasso techniques was performed, resulting in predictive generalized linear models (GLM) for each agent. Responses from the remaining intermediate samples were estimated with the predictive models, with overall predictive ability assessed by correlating predicted AUCs with their actual counterparts. Results Our integrative analysis was performed on 50 primary patient samples (36% untreated and 64% relapsed MM). Venetoclax, dinaciclib, romidepsin, panobinostat, osimertinib, belinostat and selinexor were the most active compounds in the cohort. Interestingly, LGH447, dovitinib, selinexor, JQ1, OTX-015, cobimetinib, and trametinib showed increased activity in relapsed MM when compared to untreated samples (Wilcoxon Test; p<0.05). We generated GLMs using an average of 92 markers (range 64-107) per compound, combining mRNA-sequencing expression with FISH and mutation data. The analysis proposed in the present study was validated through the unbiased selection of BCL2 among the subset of markers included in the GLM predicting sensitivity to venetoclax, a first-in-class orally bioavailable selective BCL2 inhibitor. Expression level of critical NF-kB and cell cycle genes, such as BIRC3, CKS1B, PAX5, NFKB2, and CCND2, were included in 60% of our predictive models. Mutations of DNA repair genes (ATM,TP53) were included in the GLMs of three epigenetic therapies, one histone deacetylase inhibitor and two BET inhibitors, associated to ex vivo resistance to the drugs. The presence of monosomy 13 was also a marker for ex vivo resistance for five epigenetic therapies, four HDAC inhibitors and one BET inhibitor. The three BET inhibitors, JQ1, CPI-0610, and OTX015, were among the compounds most accurately predicted by our integrative approach, with Spearman correlation values between 0.773-0.858. Overall, our models accurately predicted the ex vivo response for 16 (80%) of the compounds (r>0.7). Five (25%) of these compounds displayed a remarkably accurate prediction model in both training (highest and lowest quartiles) and validation (intermediate quartiles) samples (r>0.8). Conclusions The GLM data integration approach enabled the establishment of effective predictive models, identifying FISH, transcriptomics, and mutations of putative driver genes important in anti-MM agent responsiveness. In addition, the resulting dataset is promising for future research focusing on the discovery of novel mechanisms of action and establishing markers of sensitivity and resistance to novel compounds. We are currently increasing our dataset and seek to create an omnibus approach that predicts responses to multiple anti-MM agents simultaneously. Disclosures Bergsagel: Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Stewart:Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Ono: Consultancy; Roche: Consultancy; Seattle Genetics: Consultancy; Takeda: Consultancy.
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Pushel, Irina, Midhat S. Farooqi, Byunggil Yoo, Kai Tan, Kathrin M. Bernt, and Erin Guest. "Single-Cell Transcriptomics Reveals Similarity of Aggressive Infant Acute Lymphoblastic Leukemia Cells to Early Hematopoietic Progenitors." Blood 140, Supplement 1 (November 15, 2022): 6339–40. http://dx.doi.org/10.1182/blood-2022-170028.
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Jordana-Urriza, Lorea, Guillermo Serrano, Maria Erendira Calleja-Cervantes, Patxi San Martin-Uriz, Amaia Vilas-Zornoza, Asier Ullate-Agote, Aintzane Zabaleta, et al. "Identification of Molecular Mechanisms Governing CAR-T Cell Response in MM Patients Using Single Cell Transcriptomics." Blood 140, Supplement 1 (November 15, 2022): 7366–68. http://dx.doi.org/10.1182/blood-2022-163082.
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Li, Xing, Kevin J. Severson, Meera H. Patel, Caitlin M. Brumfiel, Alysia Hughes, David DiCaudo, Nneka Comfere, et al. "Comparative Transcriptomics of Alpha-Beta Subcutaneous Panniculitis-like T-Cell Lymphoma and Primary Cutaneous Gamma Delta T-Cell Lymphoma." Blood 136, Supplement 1 (November 5, 2020): 17. http://dx.doi.org/10.1182/blood-2020-140982.
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Background: Subcutaneous lymphomas are a heterogeneous group of diseases with variable clinical behavior that ranges from a lymphoproliferative disorder to an aggressive cytotoxic lymphoma. Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is an uncommon, often indolent cutaneous lymphoma that localizes to the subcutaneous adipose tissue. These tumors exhibit an alpha/beta phenotype (abSPTCL). Primary cutaneous gamma-delta T-cell lymphoma (PCgdTCL) is a rare, highly aggressive lymphoma that frequently involves the subcutaneous adipose tissue. The goal of this study is to identify key inflammatory and oncogenic pathways in SPTCL and PCgdTCL to determine which mutational or expression patterns differentiate abSPTCL and PCgdTCL. A comprehensive catalog of cancer gene expression profiles generated via RNA sequencing on patient samples is essential to understand tumorigenesis and to develop effective therapies. Methods: We performed transcriptomic profiling analysis using RNA sequencing in lesional skin biopsies from patients of abSPTCL (n=10), PCgdTCL (n=9), and controls (n=5). Differential analyses were performed on the entire transcriptomic profiles among the 3 groups. Results: Although the transcriptomic profiles of abSPTCL and PCgdTCL samples had similarities, there were notable differences between these groups that may elucidate unique pathways that propagate each disease. In contrast to abSPTCL, PCgdTCL samples demonstrated upregulation of cell motility and proliferation, and downregulation of pathways involving apoptosis and tumor necrosis factor production. The most significantly upregulated gene pathways in both abSPTCL and PCgdTCL samples were those involving immune and inflammatory response, viral defense, type I interferon signaling, interferon-gamma-mediated signaling, and positive regulation of T cell proliferation. The most significantly downregulated gene pathways in the two groups were those involving cell adhesion, rRNA processing and translation, and extracellular matrix organization. The top 5 upregulated immune response and downregulated cell adhesion hub genes for abSPTCL highlighted by gene interaction network analysis included: TNF, CTLA4, CD86, IL10, CCR5 & ITGB5, ITGA8, VWF, APP, CDH5, respectively. Conclusion: Our transcriptomic profiling pinpoints disease-related enrichment pathways and gene functions in abSPTCL and PCgdTCL patients, which may shed light on molecular mechanisms that drive disease activity. Hub genes in abSPTCL represent potential drug targets for further development of novel therapies. Disclosures Mangold: Kirin:Membership on an entity's Board of Directors or advisory committees;Elorac:Research Funding;Sun Pharma:Research Funding;MiRagen:Research Funding;Solagenix:Research Funding.
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Watkins, Ben, James Kaminski, Muna Qayed, Kayla Betz, Yvonne Suessmuth, Brandi Bratrude, Alison Yu, et al. "Predicting Immune Pathology after Hematopoietic Stem Cell Transplant with Transcriptomics: Naïve CD4 T Cell Expansion at Day 100 Predicts Patients with De Novo Chronic Gvhd." Blood 136, Supplement 1 (November 5, 2020): 38–39. http://dx.doi.org/10.1182/blood-2020-139488.
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Background: Chronic graft-versus-host disease (CGVHD) is the leading cause of long-term morbidity and mortality following hematopoietic stem cell transplant (HCT) and occurs in over 50% of patients undergoing unrelated donor HCT. Despite its frequency, the mechanisms driving this disease remain incompletely understood, making its prevention and successful treatment challenging. To address this issue, we have undertaken a transcriptomic analysis of T cell reconstitution after unrelated donor HCT, to dissect mechanisms driving CGVHD. Methods: The patients studied were enrolled on a Phase 2, randomized, placebo-controlled trial of abatacept for GVHD prevention in patients receiving 8/8 unrelated-donor HCT for hematologic malignancies (NCT01743131). All immune analyses in the current study were performed on patients randomized to standard GVHD prophylaxis with calcineurin inhibition + methotrexate alone (placebo cohort, n =69), and thus provide insights into the drivers of CGVHD during standard unrelated donor HCT. On Day +100, CD4+ T cells were purified from the peripheral blood of these patients, and then analyzed by RNASeq. To determine the transcriptomic drivers of CGVHD without the confounder of significant prior acute GVHD (AGVHD) or exposure to steroids, we focused on profiling the CD4+ transcriptome of de novo CGVHD (CGVHD which develops in the absence of prior grade II-IV AGVHD, n = 7) and compared these patients to those who were 'operationally tolerant' and never developed either grade II-IV AGVHD or any CGVHD (n= 4). Gene expression from the resulting transcriptomes was quantified using kallisto. Differentially expressed (DE) genes were identified using DESeq2 (threshold for DE, adjusted (for multiple testing) p <0.05). Gene Set Enrichment Analysis (GSEA) was also performed, with genes ranked by Log2FC/std_error (Log2FC), and gene signatures with an adjusted p <0.05 considered significantly enriched. Results: DE analysis identified 101 genes that were significantly upregulated in CD4+ T cells from de novo CGVHD group and 54 genes that were significantly upregulated in the 'operationally tolerant' group (Figure 1A). GSEA identified that the mostly highly enriched signatures in patients with de novo CGVHD encompassed naïve CD4+ transcriptional programing (Figure 1B-C), in agreement with flow cytometric analysis, which also demonstrated expansion of CD4+ naïve T cells at Day +100 in patients developing de novo CGVHD compared to those demonstrating operational tolerance (Figure 1D). Importantly, the naïve CD4+ T cell signatures that were identified were distinct from those defining CD4+ stem cell memory T cells (which did not enrich in the de novo CGVHD cohort). In contrast, the gene signature of the operationally tolerant patients were enriched for regulatory gene sets (Figure 1C), consistent with a large body of evidence demonstrating that Treg expansion can be protective against CGVHD. Discussion: This study represents, to our knowledge, the first interrogation of the transcriptomic features of patients developing de novo CGVHD versus those operationally tolerant patients who develop neither significant AGVHD nor CGVHD after HCT. These patients may represent a particularly effective cohort in which to study immunologic drivers of CGVHD, given their freedom from prior treatment with corticosteroids, which can confound downstream transcriptomic analyses. Our data provide compelling evidence for a prominent naïve CD4+ T cell signature in patients who develop moderate-to-severe CGVHD despite their lack of antecedent AGVHD. These results are provocative, as they implicate a cell subset that is often considered more quiescent (naïve T cells) as associated with patients who develop immune pathology associated with CGVHD. These results suggest that naïve CD4+ T cells may represent a potent reservoir for alloreactivity, that, once activated, can cause significant disease. This would be in agreement with the implications of previously reported trials of naïve T cell depletion, which resulted in significant control of CGVHD. These results suggest that strategies to restrain naïve T cell pathogenic activation after Day +100 may improve CGVHD outcomes, and that the CD4+ T cell transcriptomic signature at this timepoint could be developed into a robust immunologic biomarker for the risk of developing CGVHD versus operational tolerance after HCT. Figure 1 Disclosures Watkins: Bristol Myers Squib: Honoraria. Qayed:Novartis: Consultancy; Mesoblast: Consultancy. Blazar:Tmunity: Other: Co-founder; KidsFirst Fund: Research Funding; BlueRock Therapeutics: Research Funding; Childrens' Cancer Research Fund: Research Funding; BlueRock Therapeuetic: Consultancy; Magenta Therapeutics: Consultancy; Fate Therapeutics Inc.: Research Funding. Horan:Bristol Myers Squib: Honoraria, Research Funding. Langston:Kadmon Corporation: Research Funding; Astellas Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Research Funding; Incyte: Research Funding; Bristol Myers Squib: Research Funding; Chimerix: Research Funding; Takeda: Research Funding. Kean:fortyseven: Consultancy; regeneron: Research Funding; hifibio: Consultancy; kymab: Consultancy; Bristol Meyers Squibb: Research Funding; gilead: Research Funding; novartis: Consultancy; bluebird bio: Research Funding; magenta: Research Funding.
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Pellegrini, Laura, Claudia Bonfio, Jessica Chadwick, Farida Begum, Mark Skehel, and Madeline A. Lancaster. "Human CNS barrier-forming organoids with cerebrospinal fluid production." Science 369, no. 6500 (June 11, 2020): eaaz5626. http://dx.doi.org/10.1126/science.aaz5626.
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Cerebrospinal fluid (CSF) is a vital liquid, providing nutrients and signaling molecules and clearing out toxic by-products from the brain. The CSF is produced by the choroid plexus (ChP), a protective epithelial barrier that also prevents free entry of toxic molecules or drugs from the blood. Here, we establish human ChP organoids with a selective barrier and CSF-like fluid secretion in self-contained compartments. We show that this in vitro barrier exhibits the same selectivity to small molecules as the ChP in vivo and that ChP-CSF organoids can predict central nervous system (CNS) permeability of new compounds. The transcriptomic and proteomic signatures of ChP-CSF organoids reveal a high degree of similarity to the ChP in vivo. Finally, the intersection of single-cell transcriptomics and proteomic analysis uncovers key human CSF components produced by previously unidentified specialized epithelial subtypes.
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Treaba, Diana O., Dennis M. Bonal, Anna D. Chorzalska, Christoph Schorl, Kelsey Hopkins, John L. Reagan, Peter Rintels, Peter J. Quesenberry, and Patrycja M. Dubielecka. "Levels of Osteopontin (SPP1), Osteonectin (SPARC) and Biglycan (BGN) in Acute Myeloid Leukemia Bone Marrow Biopsies Post-Induction Therapy Define the Status of Osteogenic Niche and Show Inverse Correlation with Therapeutic Response." Blood 136, Supplement 1 (November 5, 2020): 29–30. http://dx.doi.org/10.1182/blood-2020-142604.
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Background: Acute Myeloid Leukemia (AML) has a five-year survival rate of 25% and its high mortality is linked to poor response to treatment and relapse. Our understanding of the molecular mechanisms controlling relapse and AML progression is limited. Animal models indicate that AML cells significantly modulate their bone marrow microenvironment inducing gradual loss of endosteal and vascular niches, both playing critical roles in support and maintenance of normal hematopoiesis. The goal of this study was to determine microenvironmental factors driving the gradual retraction of endosteal and vascular niches directly in the AML core bone marrow biopsies, and assess the treatment effect on hematopoietic and non-hematopoietic cells. Methods: Transcriptomics and histopathologic evaluations of matched human AML core bone marrow biopsies obtained at diagnosis (n=12) and day 14 post-induction therapy (n=12) with daunorubicin and cytarabine (7+3) were performed. Based on post-treatment frequency of blasts in the AML bone marrow aspirate, patients were classified as responders (<5% blasts) or non-responders (> 5% blasts). Three of 6 responders (3 men, 3 women, average age 59 yrs) had normal karyotype, and three of 6 non-responders (1 man, 5 women, average age 52.6 yrs), had normal karyotype. RNA was isolated from the core bone marrow biopsies and subjected to Clariom D Human Affymetrix arrays. Transcriptomics data were analyzed using Affymetrix Transcriptome Analysis Console with LIMMA R package and Gene Set Enrichment Analysis (GSEA). H&E stained bone marrow biopsy slides were subjected to blinded histopathological assessment. Results: Transcriptomic data analysis of responder vs. non-responder samples at diagnosis indicated significant loss of transcripts associated with heme metabolism (HBB, HBD, GYPE, CA1) suggesting decrease in frequency of erythroid progenitors (Fig.A). Trends of decreased frequency of erythroid progenitors were noted in both bone marrow biopsies and aspirates of diagnostic non-responder samples (Fig.B). Decreased frequency of lymphoid cells was also noted (Fig.B). Interestingly, while post-treatment we noted a relative increase in frequencies of lymphoid cells in both responder and non-responder samples, the increase was more prominent in responders (Fig.B). Trilineage hematopoiesis appeared affected more in diagnostic and post-treatment responder samples. Transcriptome analyses of diagnostic vs. post-treatment responder samples indicated significant increase in transcripts associated with activity within endosteal niche (SPARC, SPP1, DCN, VCAN, BGN) and significant loss of transcripts associated with DNA replication (TOP2, HELLS, E2F8) (Fig.C), the latter was consistent with treatment-related loss of cellularity. Only modest increase in SPARC, SPP1 or BGN levels and no significant decrease in DNA-replication associated transcripts were noted in non-responder post-treatment samples (Fig.1D). These data indicate greater loss of AML cells and greater activity within the endosteal niche in responder in comparison to non-responder samples. Finally, analyses performed on post-treatment responder vs. non-responder samples showed significant decrease in SPARC, SPP1, DCN, VCAN, BGN in non-responder post-treatment samples (Fig.E, F). Endosteal niche in histopathologic evaluation at diagnosis was generally unremarkable in both responder and non-responder samples with only rare osteoblasts present. In contrast, post-treatment, we found an elevated number of osteoblasts in responders in comparison to non-responder samples (Fig.G, H). Conclusions: Transcriptomic and histopathologic analyses of AML bone marrow biopsies procured at diagnosis and post-treatment from responder or non-responders indicate inverse correlation between the activity of endosteal niche and levels of transcripts involved in osteoblast maturation and homeostasis. Significant suppression of mesenchymal/osteoblast component of the niche is observed in non-responder samples. To our knowledge this is a first report showing the correlation between levels of osteopontin (SPP1), osteonectin (SPARC) and biglycan (BGN) and response to chemotherapy directly in the AML core bone marrow biopsies. Our data suggest that osteo-stimulatory factors could be used to achieve better therapeutic outcomes in AML. Disclosures No relevant conflicts of interest to declare.
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van Bergen, Cornelis A. M., Marvyn T. Koning, Edwin Quinten, Agnieszka Mykowiecka, Julieta Sepulveda, Ramin Monajemi, Ruben A. L. De Groen, et al. "High-Throughput BCR Sequencing and Single-Cell Transcriptomics Reveal Distinct Transcriptional Profiles Associated with Subclonal Evolution of Follicular Lymphoma." Blood 134, Supplement_1 (November 13, 2019): 298. http://dx.doi.org/10.1182/blood-2019-130508.
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Objectives: Follicular lymphoma (FL) typically originates from premalignant mature B cells that carry the founder t(14;18) BCL2 translocation. Mutations in epigenetic modifiers and acquisition of N-glycosylation sites in CDR regions of the B-cell receptor (BCR) are recurrent secondary events in FL pathogenesis. Despite these oncogenic drivers, FL can remain indolent and clinically stable for years. The molecular events driving subclonal evolution into symptomatic progression and eventual transformation to aggressive lymphoma are insufficiently understood. FL cells are frozen in their B-cell development at the germinal center stage and undergo continuous somatic hypermutation mediated by expression of activation-induced deaminase (AID). We aim to identify crucial drivers of subclonal FL evolution by high-throughput mapping at single-cell resolution. Methods: Viable FL cells were isolated and cryopreserved from 23 histologically or immunocytologically confirmed FL samples from 13 patients with informed consent. Full-length VDJ/VJ transcripts were isolated by unbiased template-switching ARTISAN PCR and massive parallel NGS sequencing on the PacBio platform. The clonal primordial FL BCR (pBCR) was reconstructed from unmutated IGV/IGJ sequences with the CDR3 of the least mutated BCR. Since the IgTree program was unable to process the obtained numbers of BCR sequences, we developed the WILLOW algorithm for analysis of BCR evolution based on the principle of maximum parsimony and on distance from the pBCR. Intraclonal BCR variability was quantified by Shannon's diversity index. 5' single cell transcriptomics and VDJ/VJ sequencing was performed on 2 pools of highly purified FL cells from 5 lymph node biopsies on the 10x Genomics platform. Data were deconvoluted based on expressed variants by the Single Cell Sample Matcher (SCSM) algorithm. Clustering based on gene expression profiles was performed by shared nearest neighbour (SNN) modularity optimization within the R Seurat package. Genes whose expression differed significantly (adjusted p<0.05) between clusters were assigned to gene ontology terms. Results: ARTISAN PCR/PacBio NGS yielded a median of 743 full-length VDJ and VJ sequences (range 62-12782) per BCR chain with expected high intraclonal diversity (median 200 subclones, range 15-3301). WILLOW revealed dominant FL subclones with a subclonal hierarchy wherein multiple routes converged to offspring nodes with identical additional mutations rather than tree-like branching (Figure). In serial samples of 4 patients, lymph node biopsies had only marginally higher subclonal diversity than blood or bone marrow samples (p=0,055; Wilcoxon's matched-pairs signed rank test). Overall BCR mutational burden increased over time in sequential biopsies. Two cases of histological FL transformation were dominated by a single subclone (65% and 80% of all VDJ/VJ sequences, respectively) that was rare in the preceding FL BCR network (0.2% and 1.8%). Pooled transcriptomics data from 6050-6500 cells were assigned to individual samples by SCSM and revealed up to seven transcriptional clusters per FL. In 9 of 10 FL, genes assigned to immune function strongly contributed to separation into one or more clusters. Single cell VDJ/VJ sequencing yielded combined heavy and light chain BCR sequences for a median of 502 FL cells per biopsy (range 22 - 1919) that permitted mapping of subclonal evolution by WILLOW based on complete BCR information. Transcriptome clusters were not distributed evenly throughout the WILLOW FL BCR networks but rather statistically associated with distinct major FL subclones. Vice versa, major FL subclones within the same biopsy were distinguished by particular gene expression profiles. Conclusions: WILLOW facilitates mapping of subclonal FL evolution based on high-throughput BCR sequencing. FL evolution proceeds in networks rather than tree-like branching, whereby acquisition of certain combinations of several BCR mutations can occur in parallel in different trajectories. Transcriptomic profiling of single FL cells identifies distinct clusters within a single biopsy. Mapping of these clusters to the FL cell position in the subclonal FL evolutionary network identifies putative mechanisms that are associated with subclonal progression. These mechanisms involve physiological B-cell signalling pathways. Figure Disclosures No relevant conflicts of interest to declare.
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Chaussabel, Damien. "Assessment of immune status using blood transcriptomics and potential implications for global health." Seminars in Immunology 27, no. 1 (February 2015): 58–66. http://dx.doi.org/10.1016/j.smim.2015.03.002.
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Gobert, Geoffrey N., Russell McInnes, Luke Moertel, Charles Nelson, Malcolm K. Jones, Wei Hu, and Donald P. McManus. "Transcriptomics tool for the human Schistosoma blood flukes using microarray gene expression profiling." Experimental Parasitology 114, no. 3 (November 2006): 160–72. http://dx.doi.org/10.1016/j.exppara.2006.03.003.
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Flohr Svendsen, Arthur, Daozheng Yang, KyungMok Kim, Seka Lazare, Natalia Skinder, Erik Zwart, Anna Mura-Meszaros, et al. "A comprehensive transcriptome signature of murine hematopoietic stem cell aging." Blood 138, no. 6 (April 19, 2021): 439–51. http://dx.doi.org/10.1182/blood.2020009729.
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Abstract We surveyed 16 published and unpublished data sets to determine whether a consistent pattern of transcriptional deregulation in aging murine hematopoietic stem cells (HSC) exists. Despite substantial heterogeneity between individual studies, we uncovered a core and robust HSC aging signature. We detected increased transcriptional activation in aged HSCs, further confirmed by chromatin accessibility analysis. Unexpectedly, using 2 independent computational approaches, we established that deregulated aging genes consist largely of membrane-associated transcripts, including many cell surface molecules previously not associated with HSC biology. We show that Selp (P-selectin), the most consistent deregulated gene, is not merely a marker for aged HSCs but is associated with HSC functional decline. Additionally, single-cell transcriptomics analysis revealed increased heterogeneity of the aged HSC pool. We identify the presence of transcriptionally “young-like” HSCs in aged bone marrow. We share our results as an online resource and demonstrate its utility by confirming that exposure to sympathomimetics or deletion of Dnmt3a/b molecularly resembles HSC rejuvenation or aging, respectively.
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Potdar, Alka A., Michael Caponegro, Onuralp Soylemez, Anupama Reddy, and Karen Yu. "Investigating Gene Expression Signatures and Cell-Type Composition Changes in Sickle Cell Disease Using Whole Blood Transcriptomics." Blood 140, Supplement 1 (November 15, 2022): 5403–4. http://dx.doi.org/10.1182/blood-2022-167738.
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Nelson, Jonathan W., Mohammed Z. Ferdaus, James A. McCormick, Jessica Minnier, Sanjiv Kaul, David H. Ellison, and Anthony P. Barnes. "Endothelial transcriptomics reveals activation of fibrosis-related pathways in hypertension." Physiological Genomics 50, no. 2 (February 1, 2018): 104–16. http://dx.doi.org/10.1152/physiolgenomics.00111.2017.
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Hypertension poses a significant challenge to vasculature homeostasis and stands as the most common cardiovascular disease in the world. Its effects are especially profound on endothelial cells that form the inner lining of the vasculature and are directly exposed to the effects of excess pressure. Here, we characterize the in vivo transcriptomic response of cardiac endothelial cells to hypertension by rapidly isolating these cells from the spontaneous hypertension mouse model BPH/2J and its normotensive BPN/3J control strain and performing and RNA sequencing on both. Comparison of transcriptional differences between these groups reveals statistically significant changes in cellular pathways consistent with cardiac fibrosis found in hypertensive animals. Importantly, many of the fibrosis-linked genes identified also differ significantly between juvenile prehypertensive and adult hypertensive BPH/2J mice, suggesting that these transcriptional differences are hypertension related. We examined the dynamic nature of these transcriptional changes by testing whether blood pressure normalization using either a calcium channel blocker (amlodipine) or a angiotensin II receptor blocker (losartan) is able to reverse these expression patterns associated with hypertension. We find that blood pressure reduction is capable of reversing some gene-expression patterns, while other transcripts are recalcitrant to therapeutic intervention. This illuminates the possibility that unmanaged hypertension may irreversibly alter some endothelial transcriptional patterns despite later intervention. This study quantifies how endothelial cells are remodeled at the molecular level in cardiovascular pathology and advances our understanding of the transcriptional events associated with endothelial response to hypertensive challenge.