Статті в журналах: "Cellular deconvolution"

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Main, Martin J., and Andrew X. Zhang. "Advances in Cellular Target Engagement and Target Deconvolution." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 2 (January 20, 2020): 115–17. http://dx.doi.org/10.1177/2472555219897269.

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Menden, Kevin, Mohamed Marouf, Sergio Oller, Anupriya Dalmia, Daniel Sumner Magruder, Karin Kloiber, Peter Heutink, and Stefan Bonn. "Deep learning–based cell composition analysis from tissue expression profiles." Science Advances 6, no. 30 (July 2020): eaba2619. http://dx.doi.org/10.1126/sciadv.aba2619.

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We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

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Sosina, Olukayode A., Matthew N. Tran, Kristen R. Maynard, Ran Tao, Margaret A. Taub, Keri Martinowich, Stephen A. Semick, et al. "Strategies for cellular deconvolution in human brain RNA sequencing data." F1000Research 10 (August 4, 2021): 750. http://dx.doi.org/10.12688/f1000research.50858.1.

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Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

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Diaz, Michael, Jasmine Tran, Nicole Natarelli, Akash Sureshkumar, and Mahtab Forouzandeh. "Cellular Deconvolution Reveals Unique Findings in Several Cell Type Fractions Within the Basal Cell Carcinoma Tumor Microenvironment." SKIN The Journal of Cutaneous Medicine 7, no. 6 (November 13, 2023): 1170–73. http://dx.doi.org/10.25251/skin.7.6.15.

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Introduction: Despite therapeutic advancements, locally advanced and metastatic basal cell carcinomas continue to carry poor prognoses and high recurrence rates. Current treatment options remain suboptimal due to limited efficacy and associated adverse events. The objectives of this study are to 1) characterize the basal cell carcinoma immune cell microenvironment and 2) identify novel therapeutic targets. Methods: Transcriptome data representing 25 basal cell carcinoma and 25 control tissue samples were obtained from the Gene Expression Omnibus. Cell type fraction estimates were derived by least-squares deconvolution. Population differences were determined by Mann-Whitney U test. Results: Most significantly, two deconvolution algorithms similarly observed greater B cell infiltration in tumor samples compared to normal tissue (P<0.0001). Conclusion: Importantly, the results of this study provide new insight into the basal cell carcinoma tumor microenvironment and nominate testable immune cell populations for future therapeutic discovery. Study limitations include sample size and applicable background prediction levels of bulk deconvolution tools.

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5

Kim, Boyoung. "DVDeconv: An Open-Source MATLAB Toolbox for Depth-Variant Asymmetric Deconvolution of Fluorescence Micrographs." Cells 10, no. 2 (February 15, 2021): 397. http://dx.doi.org/10.3390/cells10020397.

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To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations. This blur becomes more severe when focusing further inside the specimen as photons in deeper focus must traverse a longer distance within the specimen. This type of blur is called depth-variance. Moreover, due to lens imperfection, the blur has asymmetric shape. Most deconvolution solutions for removing blur assume depth-invariant or x-y symmetric blur, and presently, there is no open-source for depth-variant asymmetric deconvolution. In addition, existing datasets for deconvolution microscopy also assume invariant or x-y symmetric blur, which are insufficient to reflect actual imaging conditions. DVDeconv, that is a set of MATLAB functions with a user-friendly graphical interface, has been developed to address depth-variant asymmetric blur. DVDeconv includes dataset, depth-variant asymmetric point spread function generator, and deconvolution algorithms. Experimental results using DVDeconv reveal that depth-variant asymmetric deconvolution using DVDeconv removes blurs accurately. Furthermore, the dataset in DVDeconv constructed can be used to evaluate the performance of microscopy deconvolution to be developed in the future.

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Turner, J. N., B. Roysam, T. J. Holmes, D. H. Szarowski, W. Lin, S. Bhattacharyya, H. Ancin, R. Mackin, and D. Becker. "Visualization and quantitation of cellular and tissue anatomy by 3D light microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 928–29. http://dx.doi.org/10.1017/s0424820100172371.

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Many areas of biomedical research require the visualization and quantitation of 3D micro- and tissue level anatomy. Both are possible by utilizing confocal or wide-field light microscopy in combination with computational methods, including deblurring and 3D image analysis. The three-dimensionality of the specimen, required image resolution, and parameters to be quantitated dictate the methods of choice. Applications under study by our group range from automatically quantitating a few cell layers in cervical/vaginal smears, or hundreds of cell nuclei or immunologically labeled cells in thick brainslices, to tracing individual neurons for long distances in 3D space. Examples of our wide-field deconvolution methods and automated detection and counting of nuclei in thick tissue are shown.Image restoration by blind deconvolution of portions of the axonal fields of two rat hippocampal pyramidal cells injected with biocytin and contrasted with horseradish peroxidase and diaminobenzidine6 is shown in Figure 1. The original data is projected along and perpendicular to the optic axis in Fig. 1a while 1b is a similar set of restored image projections.

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7

Abbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee, and Hilary F. Clark. "Deconvolution of Blood Microarray Data Elucidates Cellular Activation Patterns in SLE." Clinical Immunology 123 (2007): S125—S126. http://dx.doi.org/10.1016/j.clim.2007.03.536.

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8

Udpa, L., V. M. Ayres, Yuan Fan, Qian Chen, and S. A. Kumar. "Deconvolution of atomic force microscopy data for cellular and molecular imaging." IEEE Signal Processing Magazine 23, no. 3 (May 2006): 73–83. http://dx.doi.org/10.1109/msp.2006.1628880.

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9

Blum, Yuna, Marie-Claude Jaurand, Aurélien De Reyniès, and Didier Jean. "Unraveling the cellular heterogeneity of malignant pleural mesothelioma through a deconvolution approach." Molecular & Cellular Oncology 6, no. 4 (May 7, 2019): 1610322. http://dx.doi.org/10.1080/23723556.2019.1610322.

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Poirier, Christopher C., Win Pin Ng, Douglas N. Robinson, and Pablo A. Iglesias. "Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression." PLoS Computational Biology 8, no. 4 (April 26, 2012): e1002467. http://dx.doi.org/10.1371/journal.pcbi.1002467.

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Mahon, Kerry P., Terra B. Potocky, Derek Blair, Marc D. Roy, Kelly M. Stewart, Thomas C. Chiles, and Shana O. Kelley. "Deconvolution of the Cellular Oxidative Stress Response with Organelle-Specific Peptide Conjugates." Chemistry & Biology 14, no. 8 (August 2007): 923–30. http://dx.doi.org/10.1016/j.chembiol.2007.07.011.

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12

Alonso-Moreda, Natalia, Alberto Berral-González, Enrique De La Rosa, Oscar González-Velasco, José Manuel Sánchez-Santos, and Javier De Las Rivas. "Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells." International Journal of Molecular Sciences 24, no. 13 (June 28, 2023): 10765. http://dx.doi.org/10.3390/ijms241310765.

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In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

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Qin, Yufang, Weiwei Zhang, Xiaoqiang Sun, Siwei Nan, Nana Wei, Hua-Jun Wu, and Xiaoqi Zheng. "Deconvolution of heterogeneous tumor samples using partial reference signals." PLOS Computational Biology 16, no. 11 (November 30, 2020): e1008452. http://dx.doi.org/10.1371/journal.pcbi.1008452.

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Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).

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Mir, Mustafa, S. Derin Babacan, Michael Bednarz, Minh N. Do, Ido Golding, and Gabriel Popescu. "Visualizing Escherichia coli Sub-Cellular Structure Using Sparse Deconvolution Spatial Light Interference Tomography." PLoS ONE 7, no. 6 (June 28, 2012): e39816. http://dx.doi.org/10.1371/journal.pone.0039816.

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Abbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee, Zora Modrusan, and Hilary F. Clark. "Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus." PLoS ONE 4, no. 7 (July 1, 2009): e6098. http://dx.doi.org/10.1371/journal.pone.0006098.

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Marquardt, Jens U. "Deconvolution of the cellular origin in hepatocellular carcinoma: Hepatocytes take the center stage." Hepatology 64, no. 4 (July 27, 2016): 1020–23. http://dx.doi.org/10.1002/hep.28671.

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17

Rosasco, Mario G., Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Luca Lonini, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, and Ameen A. Salahudeen. "Abstract 4692: Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist." Cancer Research 83, no. 7_Supplement (April 4, 2023): 4692. http://dx.doi.org/10.1158/1538-7445.am2023-4692.

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Abstract Next-generation sequencing (NGS) of bulk cell populations is a useful and ubiquitous tool for the molecular characterization of clinical tumor samples. Bulk NGS reveals transcript abundance within a tumor sample and can further infer cell populations via deconvolution algorithms (PMID:31570899). However, it can’t ascribe the cellular context for a given gene’s expression or elucidate the spatial organization of tumor microenvironments. These additional features are critical to our understanding of tumor biology and are key to the development of immuno-oncology therapeutics. Spatial Transcriptomics (ST) is an emerging technology that characterizes gene expression within the spatial context of tissue. ST data can be generated directly from archival formalin fixed paraffin embedded samples, enabling the study of spatial gene expression in real-world clinical settings. We have studied a dataset comprising 6 samples from non-small cell lung cancer (NSCLC) patients and 1 core needle biopsy from a tumor of unknown origin. We used the 10X Visium CytAssist platform to generate ST data and additionally generated paired bulk RNAseq data. To test the interassay reliability of CytAssist on archival FFPE tissue sections, we compared ST results across 3 sample preparation conditions. We further studied the state of the tumor microenvironment by applying state-of-the-art computational approaches to deconvolve immune cell populations and produce super-resolution ST maps, validated using multiplex immunofluorescence (IF) via CODEX (PMID:30078711). We find key quality control metrics and spatial biomarkers are consistent across all 3 sample preparation conditions. When comparing deconvolution results between bulk and spatially-resolved methods we observe modest correlations for many cell types despite differences in sample preparation, supporting the idea that bulk and spatial samples contain complementary transcriptomic information. However, within samples, we find many of the correlations observed in bulk do not show strong spatial correlation. These comparisons indicate the importance of considering spatial context when studying the tumor immune microenvironment. Finally, we find an agreement between super-resolution ST and multiplex IF across key spatial biomarkers. These results demonstrate clinical archival FFPE samples yield high interassay reliability via the CytAssist platform. Results were consistent through 3 different H&E staining protocols and findings were consistent when superresolution deconvolution was utilized which further strongly correlated with high-resolution multiplex IF. Our findings demonstrate the feasibility and translational utility of ST to discover spatial signatures and the cellular context in retrospective clinical cohorts to empower discovery and translational efforts in precision oncology and therapeutic development. Citation Format: Mario G. Rosasco, Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Luca Lonini, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, Ameen A. Salahudeen. Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4692.

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Fisher, Cody, Jordan Krull, Aditya Bhagwate, Kerryl Greenwood-Quaintance, Matthew P. Abdel, and Robin Patel. "Predicted Cellularity using RNASeq-Based Cellular Deconvolution Differentiates Periprosthetic Joint Infection from Non-Infectious Arthroplasty Failure." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 170.28. http://dx.doi.org/10.4049/jimmunol.208.supp.170.28.

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Abstract Differentiation of aseptic and septic causes of arthroplasty failure is essential due to their different treatment schemes. We hypothesized that predicted cellularity profiling of sonicate fluid using the RNASeq-based the cellular deconvolution tools CIBERSORTx and ABIS-seq can differentiate between PJI and non-infected arthroplasty failure (NIAF). Profiles were created for 93 sonicate fluid samples (40 from NIAF and 53 from PJI patients) that had been subjected to RNASeq analysis. CIBERSORTx provided 22 total predicted cell types; 12 predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, activated mast cells, and eosinophils (p≤0.0004) and decreased M0 macrophages, M2 macrophages, and Treg cells (p≤0.0001). ABIS-seq provided 17 total predicted cell types; seven predicted cellular abundances were differentially expressed in PJI versus NIAF, including increased neutrophils, Vd2 γδ T cells, and basophils (p-values of <0.0001, 0.0003, and 0.0263, respectively) and decreased nonclassical/intermediate (NC+I) monocytes, mucosal-associated invariant T (MAIT) cells, and myeloid dendritic cells (mDC) (p-values ≤0.0001). Receiver operative characteristic area under the curve (AUC) analysis identified cells types most predictive of PJI (CIBERSORTx: neutrophils [AUC = 94], activated mast cells [AUC = 93]; ABIS-seq: neutrophils [AUC = 88]) and NIAF (ABIS-seq: NC+I monocytes [AUC = 85], MAIT cells [AUC = 80], mDCs [AUC = 81]). PJI and NIAF samples were differentially clustered by principal component analysis using both tools. Overall, cellularity profiling using RNASeq-based cellular deconvolution can differentiate between PJI and NIAF sonicate fluid samples. Work supported by NIAMS (NIH R01 AR056647). CF was supported by the Mayo Clinic Graduate School of Biomedical Sciences and the Ph.D. Training Grant in Basic Immunology (NIH R25 GM055252 24).

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Curtin, Lee, Kamila Bond, Sebastian Velez, Andrea Hawkins-Daarud, Javier C. Urcuyo, Gustavo De Leon, Jazlynn Langworthy, et al. "BULK RNA-SEQ DECONVOLUTION OF IMAGE-LOCALIZED HIGGRADE GLIOMA BIOPSIES REVEALS MEANINGFUL CELLULAR STATES." Neuro-Oncology 25, Supplement_3 (September 16, 2023): iii2. http://dx.doi.org/10.1093/neuonc/noad147.005.

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Abstract AIMS High-grade glioma continues to have dismal survival with current standard-of-care treatment, owing in part to its intra- and inter-patient heterogeneity. Typical diagnostic biopsies are taken from the dense tumor core to determine the presence of abnormal cells and the status of a few key genes (e.g. IDH1, MGMT). However, the tumor core is typically resected, leaving behind possibly genetically, transcriptomically and/or phenotypically distinct invasive margins that repopulate the disease. As these remaining populations are the ones ultimately being treated, it is important to know their compositional differences from the tumor core. We aim to identify the phenotypic niches defined by the relative composition of key cellular populations and understand their variation amongst patients. METHOD We have established an image-localized research biopsy study, that samples from both the invasive margin and tumor core. From this protocol, we currently have 202 samples from 58 patients with available bulk RNA-Seq, collected between Mayo Clinic and Barrow Neurological Institute. Using a single-cell reference dataset from our collaborators at Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell states for each sample. RESULTS We find that these cell state abundances connect to patient survival and show regional differences. For example, proneural glioma states were higher in invasive regions, whereas proliferative and mesenchymal states were higher in the tumor core. CONCLUSIONS Our analysis demonstrates a need to characterize the residual tissue following glioma resection to better understand the recurrent disease.

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Shah, Nameeta, Hyun Jung Park, Pranali Sonpatki, Kyung Yeon Han, Hyeon Jong Yu, Shin Wook Kim, Tamrin Chowdhury, et al. "TMIC-20. A SPATIALLY RESOLVED HUMAN GLIOBLASTOMA ATLAS REVEALS DISTINCT CELLULAR AND MOLECULAR PATTERNS OF ANATOMICAL NICHES." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v282. http://dx.doi.org/10.1093/neuonc/noad179.1086.

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Abstract Glioblastoma (GBM) is a highly plastic ecosystem where the complex interplay between different cellular components contributes to disease progression. Although single-cell RNA (scRNA)-seq has revealed remarkable cellular heterogeneity of GBM, our knowledge regarding the spatial organization of its cellular components is currently lacking. Here we created a comprehensive dataset of 115,914 spatial transcriptomes across 32 tissue sections with matched multi-omics profiling on a set of genotyped glioma samples. We present spatial maps of 56 fine-grained cellular components, including previously unrecognized subtypes of oligodendrocytes and stromal cells, and their spatial interaction networks in each GBM- associated anatomical niche. Furthermore, the deconvolution of bulk RNA-seq data using the integrated spatial and single-cell atlas revealed clinically relevant GBM ecotypes. Our data provides comprehensive insights into the cellular architecture of GBM at high spatial resolution. It will be a valuable resource to develop effective combinatorial therapies to target all tumor-fostering niches simultaneously.

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Bornot, Aurelie, Carolyn Blackett, Ola Engkvist, Clare Murray, and Claus Bendtsen. "The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens." Journal of Biomolecular Screening 19, no. 5 (January 17, 2014): 696–706. http://dx.doi.org/10.1177/1087057113518966.

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A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor–α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

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Hendriksen, Josephine, Aidan Flynn, Simone Maarup, Hans Poulsen, Ulrik Lassen, and Joachim Weischenfeldt. "TAMI-68. DECONVOLUTION OF IMMUNOTHERAPY-TREATED GLIOBLASTOMA IDENTIFIES CELLULAR HETEROGENEITY AND PLASTICITY AT THE SINGLE-CELL LEVEL." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi212. http://dx.doi.org/10.1093/neuonc/noab196.850.

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Abstract Glioblastoma is the most aggressive cancer originating in the brain with an average survival of 15 months. One of the characteristics of glioblastoma is the high level of intra-tumor heterogeneity (ITH), but the composition and complexity at the single-cell level is poorly understood. Here, we aimed to assess the effects and consequences of immune checkpoint inhibitor (ICI) on the cellular and molecular heterogeneity of glioblastoma tumors at the single cell level. In collaboration with the phase I trials unit at Rigshospitalet, we performed paired molecular analysis of glioma cells from primary and relapse surgery after ICI treatment. Samples were analyzed using single-cell RNA sequencing (scRNA-seq) as well as bulk RNA sequencing and whole exome DNA sequencing. In an effort to trace cellular lineages we developed and refined methods to a identify copy number changes using scRNA-seq. To this end, we identified clonal and subclonal tumor cell populations in each sample. We found high levels of ITH prior to treatment, both with respect to the glioblastoma subtype enrichment and the cell type-specific gene expression. Using expression-based cell-type classification, we found defined recurrent cell-type populations present at both surgery time points. The immune checkpoint treatment had consequences on the cellular phenotypes and proportions of tumor cells, suggesting a level of plasticity in the neoplastic cells. Moreover, we identified examples of clonal dynamics and sweeps following ICI treatment, pointing to potential treatment response and resistance in these populations. We additionally found a recurrent pattern of a small population showing high levels of cell cycle activation and simultaneously expressing markers of stem cell potential. In summary, we pursued single cell-focused analysis of ICI treated glioblastoma patients to study the cellular and molecular heterogeneity within and between glioblastoma patients, which pointed to recurrent patterns of cellular responses following ICI treatment.

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Hendriksen, J. D., A. Flynn, S. B. Maarup, H. S. Poulsen, U. Lassen, and J. Weischenfeldt. "P06.01.A Deconvolution of immunotherapy-treated glioblastoma identifies cellular heterogeneity and plasticity at the single-cell level." Neuro-Oncology 24, Supplement_2 (September 1, 2022): ii37. http://dx.doi.org/10.1093/neuonc/noac174.125.

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Abstract Background Glioblastoma is the most aggressive cancer originating in the brain with an average survival of 15 months. One of the characteristics of glioblastoma is the high level of intra-tumour heterogeneity (ITH), but the composition and complexity at the single-cell level is poorly understood. Here, we aimed to assess the effects and consequences of immune checkpoint inhibitor (ICI) on the cellular and molecular heterogeneity of glioblastoma tumours using at the single cell level. Material and Methods In collaboration with the phase I trials unit at Rigshospitalet, we performed paired molecular analysis of glioma cells from primary and relapse surgery after ICI treatment. Samples were analysed using single-cell RNA sequencing (scRNA-seq) as well as bulk RNA sequencing and whole exome DNA sequencing. Results In an effort to trace cellular lineages we developed and refined methods to a identify copy number changes using scRNA-seq. To this end, we identified clonal and subclonal tumour cell populations in each sample. We found high levels of ITH prior to treatment, both with respect to the glioblastoma subtype enrichment and the cell type-specific gene expression. Using expression-based cell-type classification, we found defined recurrent cell-type populations present at both surgery time points. The immune checkpoint treatment had consequences on the cellular phenotypes and proportions of tumour cells, suggesting a level of plasticity in the neoplastic cells. Moreover, we identified examples of clonal dynamics and sweeps following ICI treatment, pointing to potential treatment response and resistance in these population. Conclusion In summary, we pursued single cell-focused analysis of ICI treated glioblastoma patients to study the cellular and molecular heterogeneity within and between glioblastoma patients, which pointed to recurrent patterns of cellular responses following ICI treatment.

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Morilla, Ian, and Juan A. Ranea. "Mathematical deconvolution uncovers the genetic regulatory signal of cancer cellular heterogeneity on resistance to paclitaxel." Molecular Genetics and Genomics 292, no. 4 (April 6, 2017): 857–69. http://dx.doi.org/10.1007/s00438-017-1316-2.

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25

Wang, Linghua. "Developmental Deconvolution Suggests New Tumor Biology and a Tool for Predicting Cancer Origin." Cancer Discovery 12, no. 11 (November 2, 2022): 2498–500. http://dx.doi.org/10.1158/2159-8290.cd-22-0943.

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Summary: Defining the developmental origins of cancer can help uncover cellular mechanisms of cancer development and progression and identify effective treatments, but it has been challenging. In this issue of Cancer Discovery, Moiso and colleagues constructed a developmental map of 33 cancer types, based on which they deconvoluted tumors into developmental components and constructed a deep learning classifier capable of high- accuracy tumor type prediction. See related article by Moiso et al., p. 2566 (4) .

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Chu, Tinyi, Edward Rice, Hans Salamanca, Zhong Wang, Sharon Longo, Robert Corona, Mariano Viapiano, Lawrence Chin, and Charles Danko. "COMP-14. MOLECULAR PROFILING AND CELLULAR DECONVOLUTION OF GLIOBLASTOMA BRAIN TUMORS USING CHROMATIN RUN-ON AND SEQUENCING." Neuro-Oncology 21, Supplement_6 (November 2019): vi64. http://dx.doi.org/10.1093/neuonc/noz175.257.

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Abstract Glioblastoma is among the most heterogeneous malignancies, making difficult the identification of clinically-relevant interactions between tumor cells and their supportive tumor microenvironment. Moreover, whether the heterogeneity of tumor cells is reflected by changes in the composition of the tumor microenvironment remains poorly defined. To further understand the cellular heterogeneity of GBM, we used our previously validated chromatin run-on and sequencing (ChRO-seq) method to analyze 61 GBMs from a retrospective cohort of patients banked at the State University of New York (Upstate Medical Center) between 1987 and 2007 (characteristics: Male:Female ratio= 2:1; median age at diagnosis= 59 years; median KPS=80; median overall survival= 343 days). We developed a new Bayesian statistical model that uses transcription at cell-type specific enhancers to identify the cellular composition of the tumor microenvironment in each patient. We validated our tool using simulations and scATAC-seq data from the same specimens, showing large improvements in sensitivity and accuracy compared with CYBERSORT. Integrative analysis of cellular composition and matching clinical data revealed correlations between the presence of specific cell types in the tumor mass and clinical variables. Finally, our analysis allowed us to identify transcription factors (e.g., NF-kB, C/EBPB) that control gene expression changes, revealing which cell types are controlled by each transcription factor in the GBM microenvironment. Our study uncovers new insights into the cellular heterogeneity of GBM and its impact on clinical progression and survival.

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Vallania, Francesco, Karen Assayag, Peter Ulz, Adam Drake, Hayley Warsinske, John St John, Girish Putcha, et al. "Plasma-derived cfDNA to reveal potential biomarkers of response prediction and monitoring in non-small cell lung cancer (NSCLC) patients on immunotherapy." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 9588. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.9588.

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9588 Background: Immune checkpoint inhibitors have shown promising results in many advanced cancers, but the response rate remains low. Various molecular and cellular biomarkers, such as elevated tumor-infiltrating cytotoxic T cells and Natural Killer (NK) cells at baseline, are associated with response. Blood-based biomarkers to predict or monitor response remain challenging to develop. Here we investigate the potential of cell-free DNA (cfDNA) biomarkers to predict response to the PD-1 immune checkpoint inhibitor nivolumab in patients with refractory metastatic non-small cell lung cancer (NSCLC). Methods: Plasma from stage IV NSCLC patients enrolled in ALCINA (NCT02866149) was collected before (baseline, BL, n = 30) and at week 8 (W8, n = 17) of nivolumab therapy. Response was determined using RECIST 1.1 (responders n = 5; non-responders n = 25). Whole-genome sequencing was performed to characterize cfDNA fragments. Tumor fraction (TF) was assessed using ichorCNA. Cellular composition was estimated by deconvolution of cfDNA co-fragmentation patterns, and transcription factor activity was estimated by measuring binding site accessibility across the genome. Results: Although estimated TF at baseline did not predict response to nivolumab, NK cell levels estimated by cell-mixture deconvolution were significantly higher in responders at BL (p < 0.05). Furthermore, estimated monocyte levels at W8 strongly correlated with overall survival (r = 0.75, p < 0.0005, HR = 15.02) and were significantly higher in responders (p < 0.05). By evaluating changes in transcription factor binding activity, we identified factors with greater accessibility in non-responders at baseline (DEAF1, THAP11) and W8 (DUX4, PDX-1). Conclusions: Plasma cfDNA signatures may be useful for response prediction and monitoring in NSCLC patients on immunotherapy. Our results suggest that changes in the immune system, as reflected by cellular composition and transcriptional activity inferred from cfDNA, may provide biological insights beyond TF alone that may benefit biomarker discovery and drug target identification.

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Wang, Kun, Sushant Patkar, Joo Sang Lee, E. Michael Gertz, Welles Robinson, Fiorella Schischlik, David R. Crawford, Alejandro A. Schäffer, and Eytan Ruppin. "Deconvolving Clinically Relevant Cellular Immune Cross-talk from Bulk Gene Expression Using CODEFACS and LIRICS Stratifies Patients with Melanoma to Anti–PD-1 Therapy." Cancer Discovery 12, no. 4 (January 4, 2022): 1088–105. http://dx.doi.org/10.1158/2159-8290.cd-21-0887.

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Abstract The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell type–specific gene expression in each sample from bulk expression, and LIRICS (Ligand–Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand–receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method CIBERSORTx. Second, analyzing The Cancer Genome Atlas, we uncover cell type–specific ligand–receptor interactions uniquely associated with mismatch-repair deficiency across different cancer types, providing additional insights into their enhanced sensitivity to anti–programmed cell death protein 1 (PD-1) therapy compared with other tumors with high neoantigen burden. Finally, we identify a subset of cell type–specific ligand–receptor interactions in the melanoma TME that stratify survival of patients receiving anti–PD-1 therapy better than some recently published bulk transcriptomics-based methods. Significance: This work presents two new computational methods that can deconvolve a large collection of bulk tumor gene expression profiles into their respective cell type–specific gene expression profiles and identify cell type–specific ligand–receptor interactions predictive of response to immune-checkpoint blockade therapy. This article is highlighted in the In This Issue feature, p. 873

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Mahoney, Rebecca, Cathal Seoighe, and Derek W. Morris. "2. USING CELLULAR DECONVOLUTION TO INVESTIGATE CELL SUBTYPE PROPORTIONS IN CORTICAL GENE EXPRESSION DATA IN SCHIZOPHRENIA." European Neuropsychopharmacology 51 (October 2021): e41. http://dx.doi.org/10.1016/j.euroneuro.2021.07.095.

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Håkanson, Maria, Stefan Kobel, Matthias P. Lutolf, Marcus Textor, Edna Cukierman, and Mirren Charnley. "Controlled Breast Cancer Microarrays for the Deconvolution of Cellular Multilayering and Density Effects upon Drug Responses." PLoS ONE 7, no. 6 (June 29, 2012): e40141. http://dx.doi.org/10.1371/journal.pone.0040141.

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Friman, Tomas. "Mass spectrometry-based Cellular Thermal Shift Assay (CETSA®) for target deconvolution in phenotypic drug discovery." Bioorganic & Medicinal Chemistry 28, no. 1 (January 2020): 115174. http://dx.doi.org/10.1016/j.bmc.2019.115174.

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Kirita, Yuhei, Haojia Wu, Kohei Uchimura, Parker C. Wilson, and Benjamin D. Humphreys. "Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury." Proceedings of the National Academy of Sciences 117, no. 27 (June 22, 2020): 15874–83. http://dx.doi.org/10.1073/pnas.2005477117.

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After acute kidney injury (AKI), patients either recover or alternatively develop fibrosis and chronic kidney disease. Interactions between injured epithelia, stroma, and inflammatory cells determine whether kidneys repair or undergo fibrosis, but the molecular events that drive these processes are poorly understood. Here, we use single nucleus RNA sequencing of a mouse model of AKI to characterize cell states during repair from acute injury. We identify a distinct proinflammatory and profibrotic proximal tubule cell state that fails to repair. Deconvolution of bulk RNA-seq datasets indicates that this failed-repair proximal tubule cell (FR-PTC) state can be detected in other models of kidney injury, increasing during aging in rat kidney and over time in human kidney allografts. We also describe dynamic intercellular communication networks and discern transcriptional pathways driving successful vs. failed repair. Our study provides a detailed description of cellular responses after injury and suggests that the FR-PTC state may represent a therapeutic target to improve repair.

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Hara, Toshiro, Rony Chanoch-Myers, Nathan Mathewson, Chad Myskiw, Lyla Atta, Lillian Bussema, Stephen Eichhorn, et al. "TAMI-12. CANCER-IMMUNE CELL INTERACTIONS DRIVE TRANSITIONS TO MESENCHYMAL-LIKE STATES IN GLIOBLASTOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi200. http://dx.doi.org/10.1093/neuonc/noab196.796.

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Abstract Communication between cancer cells and immune cells is a key determinant of the glioblastoma ecosystem and its response to therapies, but remains poorly understood. Here we leveraged single-cell RNA-sequencing (scRNA-seq) of human samples and mouse models, deconvolution analysis of bulk specimens from The Cancer Genome Atlas (TCGA) and functional approaches to dissect cellular states and cross-talk in glioblastoma. We demonstrate that macrophages induce a transition of glioblastoma cells into mesenchymal-like (MES-like) states. This effect is mediated, both in vitro and in vivo, by macrophage-derived Oncostatin M (OSM) and its cognate receptor OSMR on glioblastoma cells. We show that MES-like glioblastoma states are associated with increased T cells cytotoxicity and potentially with better clinical response to immunotherapies. Overall, our work dissects the cellular interactions within the glioblastoma microenvironment, with potential implications for therapies.

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LAI, DARONG, HONGTAO LU, MARIO LAURIA, DIGEO DI BERNARDO, and CHRISTINE NARDINI. "MANIA: A GENE NETWORK REVERSE ALGORITHM FOR COMPOUNDS MODE-OF-ACTION AND GENES INTERACTIONS INFERENCE." Advances in Complex Systems 13, no. 01 (February 2010): 83–94. http://dx.doi.org/10.1142/s0219525910002451.

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Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based solely on high-throughput gene expression data. The algorithm can infer (i) gene–gene interactions from steady state expression profiles and (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify both information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one.

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Dilip, Deepika, Pallavi Galera, David Nemirovsky, Morgan Lallo, Kamal Menghrajani, Andriy Derkach, Ross L. Levine, Richard Koche, Wenbin Xiao, and Jacob Glass. "Precision Lineage Deconvolution in Mixed Phenotype Acute Leukemia Using Cite-Seq Derived Hematopoietic Stages Identifies Lineage Dynamics Associated with Treatment Response." Blood 142, Supplement 1 (November 28, 2023): 4324. http://dx.doi.org/10.1182/blood-2023-188871.

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Introduction: Mixed phenotype Acute Leukemia (MPAL) is challenging due to ambiguous biology and lack of formal therapeutic guidelines. Although MPAL itself is rare, mixed phenotype lineage expression in secondary AML (sAML-MP) occurs with greater frequency and is associated with poor clinical outcomes. Multimodal single cell sequencing with cellular indexing of transcriptomes and epitopes (CITE-seq) offers more precise lineage characterization and assessment of lineage dynamics. Here we demonstrate a novel, unbiased approach to quantitative multistage hematopoietic lineage assessment in MPAL and sAML-MP using CITE-seq data. In addition, we demonstrate that this approach can be translated from single cell to bulk analysis. Methods: Sample preparation: Four MPAL and 6 sAML-MP samples displaying T-cell and myeloid subsets were identified and flow sorted into T and myeloid subgroups. RNA-seq was performed on each subset and analyzed using DESeq2. ComBat was used for batch correction when integrating with disparate data sources. Single cell lineage deconvolution: CITE-seq samples were processed using Seurat. A single cell deconvolution library was constructed using previously published hematopoietic stage clustering and labeling, and applied to CITE-seq data from five MPAL samples in the same dataset. Deconvolution algorithm parameters were optimized through a bootstrapping approach on in-silico sample mixtures. Bulk and translated lineage deconvolution: Bulk lineage deconvolution was performed using a published library of 13 stages of hematopoiesis derived from healthy donors.This was applied to bulk RNA-seq data from a cohort of published MPAL samples as well as the 6 sAML-MP and 4 MPAL samples described above. A pseudobulk was created for each CITE-seq lineage stage. Bulk lineage deconvolution was then re-run on the MPAL and sAML-MP samples using this library. Clinical Outcomes: A Wilcoxon signed-rank test was used to analyze differences among deconvolution-derived patient clusters, including response to induction therapy, specific gene mutations, and likelihood of transplant. Results: Lineage evolution with treatment: Deconvolution was applied on 17,848 MPAL cells and 35,038 control PBMC/BMMC cells. Antibody Derived Tag (ADT) features were consistent with lineage deconvolution stage assignments. CD38 was negatively correlated with HSC (-0.34, p < 0.001) and LMPP (-0.18, p < 0.001) while CD34 was positively correlated with both HSC (0.11, p < 0.001) and LMPP (0.27, p < 0.001) stages. MPAL 5 in particular was assessed at diagnosis and at two later time points. Unsupervised hierarchical clustering of all timepoints resulted in 10 clusters ( Figure A). The diagnostic time point was largely contained in clusters 4 (LMPP character, N = 1545 cells, 37.13%) and 9 (HSC character, N = 756 cells, 18.16%). The majority of both relapse timepoints were within cluster 4, with 76.77% (N=357 cells) of T1 and 73.92% (N=1100 cells) of T2 located in it. Bulk Unsupervised Analysis: Unsupervised analysis of sorted MPAL (N = 8), sorted AML-MP (N = 12), and unsorted MPAL (N = 24) bulk samples resulted in six distinct clusters, each with a distinct lineage signature ( Figure B). Some were enriched for more differentiated stages such as monocyte or GMP (clusters 6, 5), while others were enriched earlier stages such as LMPP (cluster 2). Secondary AML-MP samples were notably enriched in cluster 4. We found significant associations between lineage clusters and mutations in RUNX1 (p < 0.01), FLT3 (p = 0.004). Clinical Outcomes: Bulk RNA deconvolution cluster was significantly associated with complete remission (p = 0.0009). On average, individuals with an incomplete/no response to induction chemotherapy had a higher NK signature (p = 0.001), while individuals assigned to transplant had decreased LMPP character (p = 0.0495). Among the single-cell stages, decreased CLP 2 levels were associated with a poor response (p = 0.0224) while CMP / LMPP (p = 0.040) and CD4 N2 (p = 0.022) levels were decreased in patients who were able to undergo HSCT. Conclusions: Precise identification of lineage signatures in mixed phenotype leukemias shows promise in identifying clinically meaningful biological subsets of these diseases. Prospective analysis of lineage-derived biomarkers should be performed to undertake identification of formal risk stratification and treatment schemas.

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Friedrich, Johannes, Pengcheng Zhou, and Liam Paninski. "Fast online deconvolution of calcium imaging data." PLOS Computational Biology 13, no. 3 (March 14, 2017): e1005423. http://dx.doi.org/10.1371/journal.pcbi.1005423.

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Patel, Riya Jayesh, Spencer Rosario, Sahithi Sonti, Ankita Kapoor, Deepak Vadehra, Sarbajit Mukherjee, Kannan Thanikachalam, and Renuka V. Iyer. "Genomic predictors of sensitivity to chemotherapy and immunotherapy in cholangiocarcinoma." Journal of Clinical Oncology 42, no. 3_suppl (January 20, 2024): 540. http://dx.doi.org/10.1200/jco.2024.42.3_suppl.540.

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540 Background: Cholangiocarcinoma (CCA) has seen a surge immunotherapy (IO) use. Data on the sensitivity and resistance to chemotherapy and IO in CCA is limited, making it difficult to identify predictive biomarkers for guiding treatment. We hypothesize that transcriptional signatures differentiating PD-L1 high from PD-L1 low patients (pts), can be leveraged to better understand IO sensitivity. These signatures could potentially facilitate personalized treatment strategies and mitigate therapeutic resistance. Methods: We explored transcriptomic signatures related to chemotherapy and IO sensitivity in the CCA cohort of The Cancer Genome Atlas (TCGA-CHOL). We divided the pts (n=36) into 2 groups based on PD-L1 (CD274) expression levels. Pts were designated as PD-L1 high (n=11) if PD-L1 ≥ average PD-L1 expression, and PD-L1 low (n=25) if PD-L1 ≤ average expression PD-L1in the cohort. Transcriptional dysregulation was assessed by comparing transcriptional profiles of the 2 groups. Differential expression analysis, using Limma, was utilized for Gene Set Enrichment Analysis (GSEA), to assess the predicted function of transcriptional dysregulation that occurs between the 2 groups. Immune deconvolution by using the Tumor IMmune Estimation Resource (TIMER) deconvolution software to assess differences in immune infiltration and resulting scores from this analysis were used to assess how immune cell population estimates correlated with immune factors, like PD-L1 expression. Results: GSEA revealed PD-L1 low pts showed significant alterations in metabolic pathway genes that regulate cholesterol, amino acid transport and cellular respiration. As expected, GSEA revealed alterations to immune cell pathways associated with PD-L1 expression, assessed using TIMER revealed several significant correlations between immune cell deconvolution scores: CD4+ and CD8+ T-Cell (R=-0.35, p=0.02), CD8+ and neutrophils (R=0.42, p=0.009), and CD8+ and myeloid dendritic cells (R=0.3348, p=0.04). We noted significant correlations between immune cells deconvolution scores with PD-L1 expression, including PD-L1 and B Cells (R=0.54, p=0.0005), macrophages (R=0.41, p=0.01), CD8+ T Cells (R=0.43, p=0.008), neutrophils (R=0.56, p=0.0003), and myeloid dendritic cells (R=0.48, p=0.002). Significant differences in immune deconvolution scores of CD8+ T Cells, macrophages, neutrophils were noted between PD-L1 high and low pts. Conclusions: PD-L1 high pts with CCA exhibit expected differential enrichment of immune signatures. Notably, we report for the first time that CCA PD-L1 low patients uniquely enrich for differential metabolic signatures. This could lay the groundwork for understanding mechanisms that underlie IO response differences, provide valuable insights if confirmed in IO treated cohorts and serve as evidence for the utility of metabolism-targeted therapies to overcome therapeutic resistance in CCA.

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Mukashyaka, Patience, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, and Jeffrey H. Chuang. "Abstract A032: Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology." Cancer Research 84, no. 3_Supplement_2 (February 1, 2024): A032. http://dx.doi.org/10.1158/1538-7445.canevol23-a032.

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Abstract Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular evolution, cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on manually annotated dataset of 3,862 cells. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivies. Cellos was able to accurately distinguish ratios of distinct fluorescently labeled cell populations in organoids, with < 3% deviation from seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent Hoechst stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantification by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclei morphology features associated with treatment and unique to each of the clones. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer clones beyond what arises from local cell division and organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging. Citation Format: Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A032.

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Guo, Shuai, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, et al. "Abstract 4273: Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution." Cancer Research 83, no. 7_Supplement (April 4, 2023): 4273. http://dx.doi.org/10.1158/1538-7445.am2023-4273.

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Abstract The accuracy of current deconvolution methods largely relies on the quality of cell-type expression references. However, single-cell (sc) and single-nuclei (sn) RNA-seq data used for building the reference are usually generated from independent studies that are distinct from the bulk RNA-seq data to be deconvolved. This study design inherently introduces technical confounding factors as unwanted variations, which is not fully addressed by current methods. To evaluate the impact of this variation on deconvolution accuracy, we generated a benchmark dataset where bulk and snRNA-seq profiling were performed from the same aliquot of single-nuclei that were extracted from 24 healthy retina samples. All donor eye samples were collected within six hours post-mortem and were absent of any disease. This study design guarantees the matched sequencing data to present the same cell-type compositions, so that cross-platform technical artifacts become the remaining confounding factor. We used the benchmark dataset to evaluate the performance of seven current deconvolution methods and found they performed much worse in matched real-bulk data than in matched pseudo-bulks that were summations of the single-cell data. This finding suggests that none of these methods have fully addressed the major technical artifacts between bulk and single-cell sequencing platforms. We therefore propose DeMix.SC, a new deconvolution framework that optimizes deconvolution parameters using a small set of matched bulk and sc/snRNA-seq data from the same tissue type. DeMix.SC includes two major steps. First, we measure the technical variations across genes and across platforms using the benchmark data. Second, we introduce a new weight function for each gene that produces a ranking order that accounts for both the platform-specific technical variations and cell-type specific expressions at gene level. Using the benchmark data for retina, we applied DeMix.SC to previously published human retinal RNA-seq data from 523 individuals with different stages of age-related macular degeneration (AMD). We observed that DeMix.SC can accurately capture the cell-type composition shifts in the AMD retina. DeMix.SC revealed a significant drop of rod cells as well as increased astrocytes, bipolar cells, and Müller cells in the AMD retina compared to the non-AMD group. The proportion changes of the later three minor cell types were not identified by other methods, while DeMix.SC could reveal such tendency. In summary, DeMix.SC integrates benchmark data to improve the deconvolution accuracy in retina samples. Our method is generic and can be applied to other disease conditions, such as deciphering the cell-type heterogeneity in cancer. We expect DeMix.SC will help revolutionize the downstream cell-type specific analysis of bulk RNA-seq data and identify cellular targets of human diseases. Citation Format: Shuai Guo, Xuesen Cheng, Andrew Koval, Shuangxi Ji, Qingnan Liang, Yumei Li, Leah A. Owen, Ivana K. Kim, John Weinstein, Scott Kopetz, John Paul Shen, Margaret M. DeAngelis, Rui Chen, Wenyi Wang. Integration with benchmark data of paired bulk and single-cell RNA sequencing data substantially improves the accuracy of bulk tissue deconvolution. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4273.

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Nishikawa, Toui, Masatoshi Lee, and Masataka Amau. "New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution." Scientific Reports 14, no. 1 (February 20, 2024). http://dx.doi.org/10.1038/s41598-024-54798-z.

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AbstractNumerous methods for bulk RNA sequence deconvolution have been developed to identify cellular targets of diseases by understanding the composition of cell types in disease-related tissues. However, issues of heterogeneity in gene expression between subjects and the shortage of reference single-cell RNA sequence data remain to achieve accurate bulk deconvolution. In our study, we investigated whether a new data generative method named sc-CMGAN and benchmarking generative methods (Copula, CTGAN and TVAE) could solve these issues and improve the bulk deconvolutions. We also evaluated the robustness of sc-CMGAN using three deconvolution methods and four public datasets. In almost all conditions, the generative methods contributed to improved deconvolution. Notably, sc-CMGAN outperformed the benchmarking methods and demonstrated higher robustness. This study is the first to examine the impact of data augmentation on bulk deconvolution. The new generative method, sc-CMGAN, is expected to become one of the powerful tools for the preprocessing of bulk deconvolution.

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Zhang, Zheyang, and Jialiang Huang. "Cellular deconvolution with continuous transitions." Nature Computational Science, July 13, 2023. http://dx.doi.org/10.1038/s43588-023-00489-0.

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Cai, Manqi, Molin Yue, Tianmeng Chen, Jinling Liu, Erick Forno, Xinhua Lu, Timothy Billiar, et al. "Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution." Bioinformatics, April 19, 2022. http://dx.doi.org/10.1093/bioinformatics/btac279.

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Abstract Motivation Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. Results To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from eleven single deconvolution methods, ten reference datasets, five marker gene selection procedures, five data normalizations, and two transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4,937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust, and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. Availability EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. Supplementary information Supplementary data are available at Bioinformatics online.

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Croxford, Matthew, Michael Elbaum, Muthuvel Arigovindan, Zvi Kam, David Agard, Elizabeth Villa, and John Sedat. "Entropy-regularized deconvolution of cellular cryotransmission electron tomograms." Proceedings of the National Academy of Sciences 118, no. 50 (December 7, 2021). http://dx.doi.org/10.1073/pnas.2108738118.

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Significance Cellular cryo-electron tomography suffers from severely compromised Z resolution due to the missing wedges of information not collected during the acquisition of tilt series. This paper shows that application of entropy-regularized deconvolution to transmission electron tomography substantially fills in this missing information, allowing for improved Z resolution and better interpretation of cellular structures.

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Vellame, Dorothea Seiler, Gemma Shireby, Ailsa MacCalman, Emma L. Dempster, Joe Burrage, Tyler Gorrie-Stone, Leonard S. Schalkwyk, Jonathan Mill, and Eilis Hannon. "Uncertainty quantification of reference-based cellular deconvolution algorithms." Epigenetics, December 20, 2022, 1–15. http://dx.doi.org/10.1080/15592294.2022.2137659.

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Bell-Glenn, Shelby, Jeffrey A. Thompson, Lucas A. Salas, and Devin C. Koestler. "A Novel Framework for the Identification of Reference DNA Methylation Libraries for Reference-Based Deconvolution of Cellular Mixtures." Frontiers in Bioinformatics 2 (March 21, 2022). http://dx.doi.org/10.3389/fbinf.2022.835591.

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Reference-based deconvolution methods use reference libraries of cell-specific DNA methylation (DNAm) measurements as a means toward deconvoluting cell proportions in heterogeneous biospecimens (e.g., whole-blood). As the accuracy of such methods depends highly on the CpG loci comprising the reference library, recent research efforts have focused on the selection of libraries to optimize deconvolution accuracy. While existing approaches for library selection work extremely well, the best performing approaches require a training data set consisting of both DNAm profiles over a heterogeneous cell population and gold-standard measurements of cell composition (e.g., flow cytometry) in the same samples. Here, we present a framework for reference library selection without a training dataset (RESET) and benchmark it against the Legacy method (minfi:pickCompProbes), where libraries are constructed based on a pre-specified number of cell-specific differentially methylated loci (DML). RESET uses a modified version of the Dispersion Separability Criteria (DSC) for comparing different libraries and has four main steps: 1) identify a candidate set of cell-specific DMLs, 2) randomly sample DMLs from the candidate set, 3) compute the Modified DSC of the selected DMLs, and 4) update the selection probabilities of DMLs based on their contribution to the Modified DSC. Steps 2–4 are repeated many times and the library with the largest Modified DSC is selected for subsequent reference-based deconvolution. We evaluated RESET using several publicly available datasets consisting of whole-blood DNAm measurements with corresponding measurements of cell composition. We computed the RMSE and R2 between the predicted cell proportions and their measured values. RESET outperformed the Legacy approach in selecting libraries that improve the accuracy of deconvolution estimates. Additionally, reference libraries constructed using RESET resulted in cellular composition estimates that explained more variation in DNAm as compared to the Legacy approach when evaluated in the context of epigenome-wide association studies (EWAS) of several publicly available data sets. This finding has implications for the statistical power of EWAS. RESET combats potential challenges associated with existing approaches for reference library assembly and thus, may serve as a viable strategy for library construction in the absence of a training data set.

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Hannon, Eilis, Emma L. Dempster, Jonathan P. Davies, Barry Chioza, Georgina E. T. Blake, Joe Burrage, Stefania Policicchio, et al. "Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles." BMC Biology 22, no. 1 (January 25, 2024). http://dx.doi.org/10.1186/s12915-024-01827-y.

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Abstract Background Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. Results We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer’s disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. Conclusions Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

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Kwon, Yong-Jun, Hi Chul Kim, Nam Youl Kim, Seo Yeon Choi, Sungyong Jung, and Auguste Genovesio. "High content cellular microarray for automated drug target deconvolution." BMC Proceedings 5, S1 (January 10, 2011). http://dx.doi.org/10.1186/1753-6561-5-s1-p76.

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Sutton, Gavin J., Daniel Poppe, Rebecca K. Simmons, Kieran Walsh, Urwah Nawaz, Ryan Lister, Johann A. Gagnon-Bartsch, and Irina Voineagu. "Comprehensive evaluation of deconvolution methods for human brain gene expression." Nature Communications 13, no. 1 (March 15, 2022). http://dx.doi.org/10.1038/s41467-022-28655-4.

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AbstractTranscriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

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Cai, Manqi, Jingtian Zhou, Chris McKennan, and Jiebiao Wang. "scMD facilitates cell type deconvolution using single-cell DNA methylation references." Communications Biology 7, no. 1 (January 2, 2024). http://dx.doi.org/10.1038/s42003-023-05690-5.

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AbstractThe proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD’s superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer’s disease.

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Bagka, Meropi, Hyeonyi Choi, Margaux Héritier, Hanna Schwaemmle, Quentin T. L. Pasquer, Simon M. G. Braun, Leonardo Scapozza, Yibo Wu, and Sascha Hoogendoorn. "Targeted protein degradation reveals BET bromodomains as the cellular target of Hedgehog pathway inhibitor-1." Nature Communications 14, no. 1 (July 1, 2023). http://dx.doi.org/10.1038/s41467-023-39657-1.

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AbstractTarget deconvolution of small molecule hits from phenotypic screens presents a major challenge. Many screens have been conducted to find inhibitors for the Hedgehog signaling pathway – a developmental pathway with many implications in health and disease – yielding many hits but only few identified cellular targets. We here present a strategy for target identification based on Proteolysis-Targeting Chimeras (PROTACs), combined with label-free quantitative proteomics. We develop a PROTAC based on Hedgehog Pathway Inhibitor-1 (HPI-1), a phenotypic screen hit with unknown cellular target. Using this Hedgehog Pathway PROTAC (HPP) we identify and validate BET bromodomains as the cellular targets of HPI-1. Furthermore, we find that HPP-9 is a long-acting Hedgehog pathway inhibitor through prolonged BET bromodomain degradation. Collectively, we provide a powerful PROTAC-based approach for target deconvolution, that answers the longstanding question of the cellular target of HPI-1 and yields a PROTAC that acts on the Hedgehog pathway.

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