Single-cell Multiomics Research Articles

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-cell Multi-omics Experiments.

Atherosclerosis is an inflammatory disease of the arterial regions exposed to disturbed blood flow (d-flow). D-flow regulates the expression of genes in the endothelium at the transcriptomic and epigenomic levels, resulting in proatherogenic responses. Recently, single-cell RNA sequencing (scRNAseq) and single-cell Assay for Transposase Accessible Chromatin sequencing (scATACseq) studies were performed to determine the transcriptomic and chromatin accessibility changes at a single-cell resolution using the mouse partial carotid ligation (PCL) model. As endothelial cells (ECs) represent a minor fraction of the total cell populations in the artery wall, a luminal digestion method was used to obtain EC-enriched single-cell preparations. For this study, mice were subjected to PCL surgery to induce d-flow in the left carotid artery (LCA) while using the right carotid artery (RCA) as a control. The carotid arteries were dissected out two days or two weeks post PCL surgery. The lumen of each carotid was subjected to collagenase digestion, and endothelial-enriched single cells or single nuclei were obtained. These single-cell and single-nuclei preparations were subsequently barcoded using a 10x Genomics microfluidic setup. The barcoded single-cells and single-nuclei were then utilized for RNA preparation, library generation, and sequencing on a high-throughput DNA sequencer. Post bioinformatics processing, the scRNAseq and scATACseq datasets identified various cell types from the luminal digestion, primarily consisting of ECs. Smooth muscle cells, fibroblasts, and immune cells were also present. This EC-enrichment method aided in understanding the effect of blood flow on the endothelium, which could have been difficult with the total artery digestion method. The EC-enriched single-cell preparation method can be used to perform single-cell omics studies in EC-knockouts and transgenic mice where the effect of blood flow on these genes has not been studied. Importantly, this technique can be adapted to isolate EC-enriched single cells from human artery explants to perform similar mechanistic studies.

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments

Isolation of Endothelial Cells from the Lumen of Mouse Carotid Arteries for Single-Cell Multi-Omics Experiments

Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis.

Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19.

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Abstract MP04: Single Cell Profiling Identifies IgM To MDA-LDL Producing Human B Cells And Atheroprotective Role Of CD24

Murine B-1 cells produce IgMs that inactivate oxidation-specific epitopes and protect against atherosclerosis. Despite compelling data that IgM to MDA-LDL (IgM MDA-LDL ) is inversely associated with coronary artery disease (CAD) and cardiovascular (CV) events in human, the human B cell subtype that produces IgM MDA-LDL is unknown. Here, we utilized mass cytometry to identify the 11 circulating human B cell subtypes and discovered that only the frequency of CD20 + CD27 + IgM + cells was associated with IgM MDA-LDL levels. Notably, high expression of CD24, a GPI-anchored sialoglycoprotein, was associated with IgM MDA-LDL (r = 0.57, p = 0.002) and inversely associated with CAD severity (r = -0.58, p=0.001). Adoptive transfer (AT) of sorted CD20 + CD27 + IgM + CD24hi (B 27+IgM+CD24hi ) and CD24lo/- (B 27+IgM+CD24lo/- ) human B cells into humanized mice demonstrated that CD24 is critical for trafficking to the spleen and IgM production. Bulk RNAseq coupled with pathway analysis of B 27+IgM+CD24hi and B 27+IgM+CD24lo/- cells revealed enhanced BCR and CCR6 signaling in B 27+IgM+CD24hi . Imaging flow cytometry indicated colocalization between CD24 and CCR6 which was abrogated by CD24 blocking antibody (CD24mAb). Trans-well migration and internalization assays demonstrated that CD24 blockade increased CCL20-induced CCR6 internalization (p < 0.05, n =4), and reduced CCL20-induced migration of B 27+IgM+CD24hi cells (p < 0.05, n =4). CD24mAb treatment of B 27+IgM+CD24hi cells prior to AT into humanized mice also reduced the amount of surface CCR6 on B 27+IgM+CD24hi cells in vivo (p < 0.05, n = 4). Both CCR6 knockout and CD24 inhibition of B 27+IgM+CD24hi cells reduced migration to the spleen (p < 0.05, n = 4), and plasma level of human IgM (p = 0.057, n = 4). Lastly, single cell multi-omics sequencing of PBMCs from 60 CAD subjects revealed elevated CCR6 and IgM signaling in B 27+IgM+CD24hi cells in subjects with less severe CAD. Results provide the first evidence for a role for CD24 in regulating CCR6-mediated B cell homing to the spleen, IgM production and CAD in humans. Findings may have important implications for the new CD24 immunotherapy entering clinical trials and raise the possibility that augmenting CD24 and/or CCR6 on B 27+IgM+ cells may be an effective atheroprotective strategy.

Abstract MP07: Multi Omic Atlas Of Human Coronary Artery Disease

Previous human genetic studies have provided insight into the genes and pathways involved in atherosclerotic CAD, however, there remains an incomplete understanding of the precise cell types that drive disease pathogenesis. To generate a cellular atlas for CAD, we performed CITE-seq on left human coronary arteries with obstructive CAD (n=5), CAD with coronary stenting (n=4), or controls without CAD (n=5); single-nuclei RNA sequencing was performed on a subset (n=4) of these samples. Standard histopathology was used to assess the degree of atherosclerotic plaque burden and annotate disease status (Fig b). Sequencing yielded 65,437 cells with 23 distinct cell types (Fig a) and canonical marker genes (Fig c). Within the stroma we found prominent transcriptional changes in endothelial cells and fibroblasts along with the emergence of a modulated smooth muscle cell (SMC) state in CAD tissue. The modulated SMC cells were enriched with fibroblast activator protein surface expression. Additionally, we noted the emergence of a proliferative SMC population in stented arteries. Two distinct fibroblast populations were present: one was specific to coronary arteries in GTEx and this population was enriched for SVEP1 expression in the CAD samples. Furthermore, pseudotime analysis and integration of published single-cell SMC lineage tracing data in atherosclerotic mice show that this population is SMC-derived. Within the immune cells, we find expansion of B- and T-cell populations with transcriptional changes within myeloid subsets. We found 4 distinct macrophage populations: inflammatory, TREM2, HLA-II high, and resident-like macrophages. CAD macrophages had enriched inflammatory and foam-cell like signatures compared to controls. Furthermore, foam cell genes were localized to the inflammatory and TREM2 macrophages. Together, our results provide the first multi-omic single cell atlas of human coronary artery tissue with insights into disease pathogenesis.

A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data

SummaryThe recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models, and the proposed approach experimentally discovered multimodal regulatory programs associated with latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that crossmodal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques.

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

BSCI-09. Multiomic single cell analysis reveals emerging principles of tumor immune microenvironment inherent to NSCLC brain metastases

Brain is one of the most common sites for distant metastasis of lung cancer. Treatment naïve lung cancer patients diagnosed with brain metastasis are left with very limited options. Checkpoint inhibition is a powerful immunotherapy strategy but delivers benefit only to a small population of patients. Here we harnessed the power and resolution of single cell RNA sequencing and single cell TCR/BCR sequencing to investigate the tumor immune microenvironment (TIME) of NSCLC brain metastases. We enrolled treatment naïve lung cancer patients with brain metastasis. The enrolled subjects covered different histology types and driver gene mutation status. We revealed the emerging principles of innate and adaptive immune components inherent to NSCLC brain metastases. We also uncovered several significant intercellular communication patterns that potentiates cancer cell seeding and fosters cancer cell proliferation. Those results served as a starting point to design optimal immunotherapy strategies for advanced lung cancer patients with limited options.

Capillary-Mediated Single-Cell Dispenser.

Single-cell manipulation, sorting, and dispensing into multiwell plates is useful for single-cell multiomics studies. Here, we develop a single-cell dispenser inspired by electrohydrodynamic jet printing that achieves accurate droplet generation and single-cell sorting and dispensing using fused silica capillary tubing as both the optical detection window and nozzle for droplet dispensing. Parameters that affect droplet dispensing performance-capillary inner and outer diameter, flow rate, applied voltage, and solution properties-were optimized systematically with COMSOL simulations and experimentation. Small (5-10 nL) droplets were obtained by using 100-μm inner diameter and 160-μm outer diameter capillary tubing and allowed efficient encapsulation and dispensing of single cells. We demonstrate an application of this easy-to-assemble single-cell dispenser by sorting and dispensing cells into multiwell plates for single-cell PCR analysis.

Exploiting Single-Cell Tools in Gene and Cell Therapy.

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Abstract 2788: Deep characterization of tumor infiltrating leukocytes using a combination of flow cytometry and single-cell multiomics

Abstract Cancer immunotherapies have the potential to induce durable anti-tumor immune responses; however the efficacy of these treatments varies among patients and partially depends on the complex interplay between malignant cells and non-malignant cells within the tumor microenvironment. In the tumor microenvironment, T cells play a crucial role in eliminating tumor cells and orchestrating anti-tumor immune responses; in contrast immunosuppressive cell types such as myeloid-derived suppressor cells can support tumor progression. Here we harnessed recent advances in single-cell multiomics that allow high-resolution functional characterization of intratumoral immune cells to improve our understanding of their dynamic relationships. Specifically, we utilized a One Solution Workflow to dissociate a solid tumor followed by in-depth dissection of the immune composition of the tumor microenvironment using both flow cytometry and the BD Rhapsody™ Single-Cell Analysis System. We characterized the cellular composition of tumor microenvironment using flow cytometry analysis. Importantly, by examining the expression of mRNA and surface protein markers at single-cell resolution, we unraveled the complexities of the tumor microenvironment, elucidated cellular heterogeneity and identified cell-type specific gene signatures. We propose this One Solution Workflow can be adopted to understand the transcriptional and proteomic phenotypes of tumor infiltrating leukocytes for development of novel immune therapeutic strategies for cancer. For Research Use Only. Not for use in diagnostic or therapeutic procedures. Class 1 Laser Product.BD-23279 (v1.0) 1120BD, the BD Logo and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2020 BD. All rights reserved. Citation Format: Xiaoshan Shi, Margaret Nakamoto, Aaron Middlebrook, Wei Huang, Evelyn Lo, Patrick T. Neuhoefer, Scott Bornheimer, Smita Ghanekar. Deep characterization of tumor infiltrating leukocytes using a combination of flow cytometry and single-cell multiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2788.

Abstract 3201: Single-cell trajectory analysis reveals a melanoma-driven distinct hematopoietic response in murine spleen

Abstract Growing evidence suggests that tumor progression can interfere with normal hematopoiesis and skew the host system to undergo myeloid biased changes. Hematopoietic stem and progenitor cells (HSPCs) are a rare population of precursor cells giving rise to progeny that replenish blood cells throughout life. HSPCs are mainly localized in the bone marrow niche in healthy adults, however pathophysiological conditions such as cancer can cause extramedullary hematopoiesis in organs like the spleen. In this study, we investigated tumor-driven phenotypic and molecular alterations in the HSPC compartment at a single-cell level during extramedullary hematopoiesis using single-cell multiomics. We used a B16-F10 melanoma mouse model to uncover the tumor-mediated changes. Mice with tumor-burden showed splenomegaly and alterations in the composition of the HSPC compartment in the spleen consistent with extramedullary hematopoiesis. We then isolated four HSPC populations, including Lin- Sca1+ cKit+ (LSK), common myeloid progenitor (CMP), granulocyte-macrophage progenitor (GMP) and megakaryocyte-erythrocyte progenitor (MEP) cells from the spleen and bone marrow of melanoma-burdened mice using the BD FACSMelody™ Cell Sorter. Next, we used an integrated workflow on the BD Rhapsody™ Single-Cell Analysis System for the downstream single-cell multiomics analysis. We utilized oligo-conjugated antibodies for NGS-based sample multiplexing and protein detection (BD® Single-Cell Multiplexing Kit, BD® AbSeq Assay), along with whole transcriptome analysis for a comprehensive analysis of mRNA expression. Data analysis using the X-shift algorithm paired with single-cell force-directed layout visualization enabled delineation of the developmental trajectories of HSPC populations and exploration of phenotypic and transcriptomic changes across different hematopoietic subpopulations and across distinct anatomic sites. The analysis also identified unique hematopoietic cell clusters present during melanoma-driven extramedullary hematopoiesis in the spleen. Overall, our findings highlight melanoma-driven perturbations in hematopoiesis by utilizing advanced single-cell multiomics technology. Disclaimers: For Research Use Only. Not for use in diagnostic or therapeutic procedures. Class 1 Laser Product. BD-23277 (v1.0) 1120 BD, the BD Logo, FACSMelody and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2020 BD. All rights reserved. Citation Format: Nihan Kara, Nikolay Samusik, Xiaoshan Shi, Chip Lomas, Stephanie Widmann, Aaron J. Tyznik. Single-cell trajectory analysis reveals a melanoma-driven distinct hematopoietic response in murine spleen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3201.

Identifying Cancer Biomarkers with Single-Cell Multi-Omics

Identifying Cancer Biomarkers with Single-Cell Multi-Omics

Abstract 2280: A comprehensive sample tracking and data processing workflow for next generation sequencing

Abstract The successful application of Next Generation Sequencing (NGS) to drug discovery requires systems to manage and document each step of the sequencing process from sample receipt through data generation and data processing. We combined BenchlingTM, a solution for tracking NGS lab processes, with FONDA (Framework Of Next generation sequencing Data Analysis) an internally developed data processing platform, to support multiple types of NGS data generation and processing. Benchling combines a digital notebook and a laboratory information management system (LIMS). The system documents and automates steps in the NGS process including: sample registration, nucleic acid extraction, library construction, flow cell construction, sequencer sample sheet generation and BCL2FASTQ conversion. This enables wet lab scientists to easily retrieve an appropriate protocol for each sample and sequencing library type. We connected our sequencers to Benchling in order to monitor each sequencing run and to keep track of the quality of NGS data. In addition, it generates “analysis ready sample sheet” (contains project and study information, location of FASTQ, sample species and library type) and uploads it into designated S3 buckets for data processing. Benchling dashboards provide overviews of NGS sample preparation, data generation and quality control. In summary, Benchling interconnects the original sample, the labels, the barcodes, the cDNA/DNA, the library, and all the QC results. We process NGS data using pipelines implemented in FONDA on a dockerized Amazon Web Services cloud platform. Analyses can be configured automatically from information exported by Benchling or launched manually. After data processing is completed, output files such as gene expression counts or variant calls are deposited into project-specific folders, ready for secondary analysis. In the current FONDA version (as of Nov 2020), we have developed pipelines for single cell multi-omics (CITE-seq and single-cell immune profiling) and bulk RNA-seq. The modular design of FONDA facilitates the development, the updating, and the extension of pipelines to new sequencing technologies. In summary, Benchling and FONDA enable high quality sample and NGS data flows from the lab for target identification, understanding mechanism of action, patient stratification and biomarker discovery. Availability and implementation: FONDA is implemented in Java and released under the Apache License 2.0. FONDA can be downloaded from GitHub at https://github.com/epam/fonda. Citation Format: Chandra Sekhar Pedamallu, Joon Sang Lee, Shu Yan, Adalis Maisonet, Aleksandr Sidoruk, Tengui Chen, Yulia Kamyshova, Mariia Zueva, Mark Magid, Quan Wan, Jeffrey Thompson, Valerie Zebrouck, Immanuel Gadaczek, Mikhail Alperovich, Brian McNatt, Alexei Protopopov, Donald Jackson, Jack Pollard. A comprehensive sample tracking and data processing workflow for next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2280.

Abstract 2244: Evaluation of large antibody panels in single-cell genomic immunophenotyping of fresh and preserved human leukocytes

Abstract Background: Multi-omic single cell studies are revolutionizing knowledge of the tumor immune microenvironment and positioned to detect rare cell subsets that correlate with response to immunotherapy. DNA-barcoded antibody panels enable the unambiguous classification of cell subsets via simultaneous measurement of surface proteins and transcriptome. However, single-cell sequencing performs best on fresh samples while multi-center clinical trials routinely obtain frozen specimens. Peripheral blood mononuclear cells (PBMCs) are commonly used tissues for charactering immune cell types, dynamics, and signaling in human studies. Our group has pioneered analyses of the tumor micro-environment in several cancer types, and also developed genomic capabilities with the resolution of flow cytometry over thousands of markers. We hypothesized that the development of advanced bioinformatics tools and large antibody panels (137 antibodies) can augment the analysis of frozen samples. Methods: Single cell genomics on matched fresh and frozen human PBMCs was performed using the TotalSeq-C human universal cocktail (BioLegend) and the 10x Genomics Chromium single-cell platform. We created bioinformatics pipelines to compare the quality control metrics, isotype matched control performance, and cell subtype analyses for fresh and frozen samples. Advanced visualization and analysis tools were developed to allow interrogation of the data in a flow cytometry paradigm, including complex gating strategies to identify rarer cell subtypes. Results: In terms of library quality, fresh and frozen cells had similar quality control parameters, although mean reads per cell were moderately lower for frozen vs. fresh tissue in both the RNA and antibody arms of the study. RNA and antibody data were integrated, clustered, and annotated for cell type and subtype. Cell type proportions were comparable across fresh and frozen samples. A panel of 7 antibody isotype controls demonstrated limited non-specific binding. Comparison of individual surface proteins across fresh and frozen samples revealed similar distributions in most cases, although signal was attenuated for a small number of epitopes in frozen samples. Our bioinformatics approaches allowed rare cell subtype identification with sensitivity and selectivity rivaling flow cytometry. Conclusions: Multi-modal single cell genomics, including a 137 antibody panel, and novel bioinformatics analytics reveal that fresh and frozen PBMC samples are largely concordant at both the transcriptome and cell-surface protein levels. Our pilot study is being expanded to larger scale clinical settings and may help characterize important cell populations that are associated with disease status, pharmacodynamics, and therapeutic response. Citation Format: Nathan O. Siemers, Jasmine Chen, Parminder Mankoo, Shazia Ilyas. Evaluation of large antibody panels in single-cell genomic immunophenotyping of fresh and preserved human leukocytes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2244.

Genomic cytometry is maturing but has single-cell multiomics hit puberty yet?

Genomic cytometry is maturing but has single-cell multiomics hit puberty yet?

Single cell multiomic analysis and immune cell type profiling of multiple myeloma with t(4;14).

e20013 Background: Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk (HR) MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona’s ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods: Diagnostic bone marrow (BM) samples from 14 patients with t(4;14) MM were analysed using the ESCAPE platform from Proteona which simultaneously measures gene and cell surface protein expression in single cells. Cryopreserved BM samples were stained with 65 DNA barcoded antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a known ratio for analysis using the 10x Genomics 3’ RNAseq kit. Resulting data were analyzed with Seurat and MapCell. Results: The patients had a median age of 63 years. All received novel agent based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. MMSET was overexpressed in all PCs while FGFR3 expression could be categorized into zero cells expressing FGFR3, low expression (< 10% of cells expressing FGFR3) or high expression (> 80% of cells expressing FGFR3). We also found heterogeneity in the expression of cancer testis antigens (CTA) such as FA133A and CTAG2 between PC clusters across samples. Variation in the immune microenvironment of the BM was seen across all patient samples with no correlation between cell types and PFS or OS. However, an analysis of BM samples at diagnosis and relapse in one patient showed a shift in the ratio of T cells to CD14 monocytes with a ratio of 5.7 at diagnosis compared to 0.6 at relapse. Further analysis of PCs in this patient found 8 PC populations, each containing variable numbers of cells from both the diagnostic and relapse samples. This suggests that all populations present at relapse were also present at diagnosis, although at variable proportions. Increased expression of RCAN3 (associated with cereblon depletion) was detected at relapse. Conclusions: We present the first application of single cell multi-omics immune profiling in high risk MM. The heterogeneity in expression of CTA has implications for the application of immunotherapies, while the upregulation of RCAN3 may explain failure of immunomodulatory therapy. Our small sample size may explain the lack of correlation between gene or protein expression with clinical outcomes. We propose that t(4;14) MM is a genomically and immunologically heterogeneous disease. Single cell analysis of larger cohorts is required to build on our findings.

In Vitro Glioblastoma Models: A Journey into the Third Dimension.

Simple SummaryIn this review, the thorny issue of glioblastoma models is addressed, with a focus on 3D in vitro models. In the first part of the manuscript, glioblastoma features and classification are recapitulated, in order to highlight the major critical aspects that should be taken into account when choosing a glioblastoma 3D model. In the second part of the review, the 3D models described in the literature are critically discussed, considering the advantages, disadvantages, and feasibility for each experimental model, in the light of the potential issues that researchers want to address.Glioblastoma multiforme (GBM) is the most lethal primary brain tumor in adults, with an average survival time of about one year from initial diagnosis. In the attempt to overcome the complexity and drawbacks associated with in vivo GBM models, together with the need of developing systems dedicated to screen new potential drugs, considerable efforts have been devoted to the implementation of reliable and affordable in vitro GBM models. Recent findings on GBM molecular features, revealing a high heterogeneity between GBM cells and also between other non-tumor cells belonging to the tumoral niche, have stressed the limitations of the classical 2D cell culture systems. Recently, several novel and innovative 3D cell cultures models for GBM have been proposed and implemented. In this review, we first describe the different populations and their functional role of GBM and niche non-tumor cells that could be used in 3D models. An overview of the current available 3D in vitro systems for modeling GBM, together with their major weaknesses and strengths, is presented. Lastly, we discuss the impact of groundbreaking technologies, such as bioprinting and multi-omics single cell analysis, on the future implementation of 3D in vitro GBM models.