Single-cell Protein Expression Research Articles

AML-160 Multimodal Analysis of DNA, Surface Proteins, and Intracellular Proteins in Single Cells

Recent advancements in precision medicine, although highly promising, present a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multiomics analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights into cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri® platform enables high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a novel workflow was developed to detect protein expression in addition to DNA genotypes in the same single cells. In this approach, cells are labeled with oligonucleotide-conjugated antibodies prior to single-cell encapsulation and DNA target amplification on the Tapestri system. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. The number of protein targets can range from a few targets to over 40, which is beyond the limit for a single flow cytometry run. This workflow has been successfully demonstrated on cell lines as well as clinical samples. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data were able to clearly distinguish CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs from CD34hiCD11blo and CD34loCD11bhi subpopulations that did contain the same pathogenic SNVs and CNVs. We further developed this workflow to include simultaneous detection of intracellular proteins and surface proteins along with targeted sequencing in single cells. The inclusion of intracellular protein detection enables measurement of pivotal proteins in cancer mechanisms. We believe that this novel multiomic technology will facilitate new discoveries in the complex relationship between genotype and phenotype, enabling a better understanding of disease biology, and subsequently improve the design of diagnostics and therapies.

A Single-Cell Atlas of Tumor-Infiltrating Immune Cells in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with limited treatment options. To guide the design of more effective immunotherapy strategies, mass cytometry was employed to characterize the cellular composition of the PDAC-infiltrating immune cells. The expression of 33 protein markers was examined at the single-cell level in more than two million immune cells from four types of clinical samples, including PDAC tumors, normal pancreatic tissues, chronic pancreatitis tissues, and peripheral blood. Based on the analyses, we identified 23 distinct T-cell phenotypes, with some cell clusters exhibiting aberrant frequencies in the tumors. Programmed cell death protein 1 (PD-1) was extensively expressed in CD4+ and CD8+ T cells and coexpressed with both stimulatory and inhibitory immune markers. In addition, we observed elevated levels of functional markers, such as CD137L and CD69, in PDAC-infiltrating immune cells. Moreover, the combination of PD-1 and CD8 was used to stratify PDAC tumors from The Cancer Genome Atlas database into three immune subtypes, with S1 (PD-1+CD8+) exhibiting the best prognosis. Further analysis suggested distinct molecular mechanisms for immune exclusion in different subtypes. Taken together, the single-cell protein expression data depicted a detailed cell atlas of the PDAC-infiltrating immune cells and revealed clinically relevant information regarding useful cell phenotypes and targets for immunotherapy development.

Abstract 3878: Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager

Abstract The spatial interactions between the immune system and tumor cells greatly influence antitumoral immunity. Characterization of immune cell composition and infiltration within the tumor niche informs prognosis, drug delivery efficiency, and therapeutic efficacy. However, few methods exist to query large numbers of immune biomarkers at subcellular spatial resolution. The CosMx™ Spatial Molecular Imager is the first platform to demonstrate simultaneous single-cell and subcellular detection of over 100 proteins on standard, biobanked, FFPE tissue samples. This high-plex protein panel detects key drivers of cancer progression and immune cell activation states. Here, we apply the CosMx 100-plex immuno-oncology assay on a set of breast cancer biopsies and demonstrate its quantitative and spatial capabilities. Key to CosMx protein technology is an antibody-oligonucleotide-conjugate 64-bit encoding method, not a cyclic exchange method. The encoding scheme is enabled by a 20nm hybridization-based optical barcode. The CosMx system uses a fully automated, cyclic microfluidics imaging system, high-resolution optics and 3D capability. The raw cyclic encoded 4-color tissue images are decoded using a robust automated decoding algorithm that detects protein sub-cellular localization and quantifies expression level. CosMx SMI produces protein localization maps for each target, which characterizes tissue microenvironment heterogeneity while providing spatial information. Additionally, accurate segmentation of individual cells enables spatial single-cell protein expression analysis, facilitating further mining and analyses of cellular subpopulations. The CosMx protein assay reagents were validated on multi-organ FFPE tissue microarrays and 35 human FFPE cell lines, including overexpression lines for key targets and cellular activation states, such as GITR, CD278, PD-L1, and PD-1. Benchmarking to multiple orthogonal datasets (e.g., the Human Protein Atlas, Cancer Cell Line Encyclopedia, and low-plex IHC) demonstrates that the assay is highly sensitive and specific. CosMx SMI protein assay can be coupled with SMI’s 1000-plex RNA-detection assay; together, such a multi-omics platform can generate an unprecedented information-rich view of spatial biology that could usher in novel discoveries about health and disease. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Zachary R. Lewis, Tien Phan-Everson, Gary Geiss, Mithra Korukonda, Ruchir Bhatt, Carl Brown, Dwayne Dunaway, Joseph Phan, Alyssa Rosenbloom, Brian Filanoski, Rhonda Meredith, Kan Chantranuvatana, Yan Liang, Emily Brown, Brian Birditt, Giang Ong, Hye Son Yi, Erin Piazza, Vikram Devgan, Nicole Ortogero, Patrick Danaher, Sarah Warren, Michael Rhodes, Joseph Beechem. Subcellular characterization of over 100 proteins in FFPE tumor biopsies with CosMx Spatial Molecular Imager [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3878.

Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma.

This is the first study investigating the mechanisms linked to CAR-T patient responses based on the sequential analysis of manufactured and infused CAR-T cells using single-cell RNA and protein expression data. Furthermore, our findings are the first to demonstrate an improvement of CAR-T cell efficacy with TIGIT inhibition alone. This article is highlighted in the In This Issue feature, p. 1825.

Abstract P3-05-01: Role of TRIM24 in metaplastic breast cancer and nomination of potential therapeutic targets

Abstract Background: Within the Triple-Negative Breast Cancer (TNBC) subclass, metaplastic TNBC (MpBC) tumors are comprised of heterogeneous, malignant epithelial (carcinoma) and mesenchymal (sarcoma) elements and are rare, highly aggressive, poorly differentiated and chemorefractory. There is an unmet need to develop therapies that address and improve survival of MpBC TNBC patients. We and others found that protein and mRNA levels of tripartite motif-containing protein 24 (TRIM24) are elevated in breast cancers, and aberrant expression of TRIM24 is linked to poor patient survival. Our previous in vitro studies showed that TRIM24 is a multidomain protein with histone reader function via a PHD/bromodomain and E3 ligase function via a RING domain that targets p53 for degradation.Methods: To determine if TRIM24 is oncogenic in vivo, we created a mouse model with conditional over expression (COE) of TRIM24 in mammary epithelia (Trim24COE). Trim24COE mice develop tumors, which were subjected to pathology, immunohistochemistry, global transcriptional and single-cell protein expression profiling. Additionally, we determined expression of TRIM24 in MpBC patient-derived tumor arrays and xenografts (PDX). Trim24COE tumor-derived primary cell lines and 3D spheroids were assessed for function and response to targeted inhibition.Results: TRIM24 over expression is sufficient to drive development of murine metaplastic carcinosarcomas (70% of all tumors), which are highly penetrant and lacking ER, PR and HER2 expression. Using global RNA-sequencing and selected proteomic profiling (RPPA) followed by CyTOF, we found that EMT, glycolysis, angiogenesis, G2/M checkpoint regulators and cMET/PI3K/mTOR are top up-regulated pathways. We defined a TRIM24 gene expression signature for Trim24COE tumors to probe TNBC patient expression data and found that the TRIM24 signature positively correlates with MpBC TNBC. Using a PROTAC targeting TRIM24, we showed that degradation of TRIM24 reduces cell viability compared to control, both in TRIM24-driven primary tumor cell lines and MpBC PDX cell suspensions. Carbon 13 (C13) tracing and other metabolomics were used to determine the impact of TRIM24 in metabolic pathways, using 3D spheroids derived from Trim24COE carcinosarcomas. We found that up-regulation of TRIM24 lowers ROS to promote cell survival by increased production of NADPH via multiple metabolic pathways that can be reversed by knocking-down TRIM24 or with specific metabolic inhibitors.Conclusions: TRIM24 is a PHD/bromodomain histone reader and p53-targeting E3-ubiquitin ligase that, when aberrantly over expressed, promotes tumorigenesis in vivo. A majority of Trim24-driven tumors are carcinosarcomas, highly similar to MpBC TNBC. Genomewide RNA expression and protein profiling show TRIM24 over expression activates EMT and disrupts metabolic homeostasis. Findings from our mouse model are relevant to tumors derived from MpBC patients, as PDXs show high expression of TRIM24 and respond to PROTAC treatment of TRIM24. Further establishment of TRIM24’s role in metabolic pathways to reduce ROS and increase cell survival may reveal potential combinatorial therapeutic approaches for MpBC. These findings suggest exciting opportunities to exploit the Trim24COE mouse model as a means of understanding mechanisms of MpBC tumor development and for preclinical studies of new, epigenetic-based therapeutic approaches. Citation Format: Vrutant Shah, Shucheng Miao, Clinton Yam, Jeffrey T Chang, Michelle Barton, Helen Piwnica-Worms, Stacy Moulder, Guillermina Lozano. Role of TRIM24 in metaplastic breast cancer and nomination of potential therapeutic targets [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-05-01.

Analytical kinetic model of native tandem promoters in E. coli.

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘dTSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and dTSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when dTSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when dTSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of dTSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of dTSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

AlzCode: a platform for multiview analysis of genes related to Alzheimer's disease.

Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function and clinical severity) and general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Atlas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AlzCode is freely available at: http://www.alzcode.xyz. Supplementary data are available at Bioinformatics online.

Peripheral Blood Single-Cell Genotyping and Phenotyping in Multiple Myeloma Reveals Shared Mutations across Multiple Hematopoietic Cell Lineages

Background:Multiple myeloma (MM) is a hematologic malignancy of terminally differentiated plasma cells. While most genomic studies of the disease have focused on characterizing isolated bone marrow plasma cells, few have addressed somatic mutations within the immune microenvironment. We previously demonstrated that somatic mutations are significantly more numerous in unsorted blood compared to bone marrow and that the number of mutations correlated with elevated markers of disease. We also reported that genes implicated in clonal hematopoiesis of indeterminate potential (CHIP) are commonly mutated in the blood from patients with MM.Aim:To integrate single-cell DNA mutation analysis and protein expression to map the clonal relationship between circulating plasma cells and non-plasma cells within the peripheral blood of patients with MM.Methods:Although bulk DNA sequencing can estimate clonal heterogeneity using analytic deconvolution techniques, it is unable to distinguish which mutations occur in the same clone(s) nor can it correlate genotype with immunophenotype from the same cell. To accurately describe the clonal architecture of the blood, we combined targeted single-cell DNA sequencing (scDNA-seq) coupled with protein expression to define mutations specific to cell phenotypes using the Tapestri Platform (Mission Bio). For this analysis, we developed a custom amplicon panel covering 22 frequently mutated genes in MM and/or CHIP to perform scDNA-seq and 4 antibodies targeting CD138, CD19, CD3, and CD33 for immunophenotyping or plasma cells, B cells, T cells, and myeloid cells respectively.Results:Peripheral blood mononuclear cells were isolated from 17 blood samples collected from 12 patients with MM. Three samples were collected at diagnosis before treatment, 8 were collected post-treatment during remission, and 6 were collected at relapse. Serial samples were obtained before and after autologous stem cell transplant from 4 patients. A total of 85,262 cells were sequenced uncovering 154 unique variants in 22 genes after quality filtering. There were 47 somatic variants, defined as those present in less than 40% of cells per sample. There were 52,136 total clones, defined as cells with identical genotypes, within this heterogenous population of cells. The most common non-synonymous somatic mutations were identified in genes SETD2, KMT2C, PPM1D. An immunophenotype was assigned to 82% of cells according to the protein with the most abundant number of reads. Dimensionality reduction of single-cell genotype using t-distributed stochastic neighbor embedding (t-SNE) effectively clustered cells according to sample and by patient in cases where there were multiple samples from the same individual. Patients with active myeloma and known circulating plasma cells detected by clinical flow cytometry were found to have the greatest proportion of CD138+ cells as well as the highest mutational burden. It was rare to detect the same clone in two separate samples collected from the same patient at different time points (median time be collection was 248 days) which may be due to under-sampling or clonal evolution. Surprisingly, all somatic variants detected in plasma cells, were also present in other cell types suggesting they were acquired from a common progenitor cell rather than after differentiation.Conclusions:scDNA-seq coupled with immunophenotyping of the peripheral blood of patients with MM reveals somatic variants are shared across multiple hematopoietic cell lineages including plasma cells indicating these mutational are acquired from a common progenitor. DisclosuresDurruthy-Durruthy: Mission Bio Inc.: Current Employment. Arribas-Layton: Mission Bio, Inc.: Current Employment. Wang: Mission Bio, Inc: Current Employment.

Single-cell intracellular epitope and transcript detection reveals signal transduction dynamics

SummaryTo further our understanding of how biochemical information flows through cells upon external stimulation, we require single-cell multi-omics methods that concurrently map changes in (phospho)protein levels across signaling networks and the associated gene expression profiles. Here, we present quantification of RNA and intracellular epitopes by sequencing (QuRIE-seq), a droplet-based platform for single-cell RNA and intra- and extracellular (phospho)protein quantification through sequencing. We applied QuRIE-seq to quantify cell-state changes at both the signaling and the transcriptome level after 2-, 4-, 6-, 60-, and 180-min stimulation of the B cell receptor pathway in Burkitt lymphoma cells. Using the multi-omics factor analysis (MOFA+) framework, we delineated changes in single-cell (phospho)protein and gene expression patterns over multiple timescales and revealed the effect of an inhibitory drug (ibrutinib) on signaling and gene expression landscapes.

MM-253: Single Cell Multi-Omic Analysis And Immune Cell Type Profiling Of Multiple Myeloma With t(4;14)

Context 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. Objective Here we utilized Proteona’s ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM with the goal of better understanding the biology underlying this high risk patient cohort. Design Diagnostic bone marrow (BM) samples from 13 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. Resulting data were analyzed with MapSuite tools from Proteona and Seurat. 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 low expression ( 80% of cells expressing FGFR3). Variation in the immune microenvironment of the BM was seen across all patient samples with no correlation between cell types present in the BM and PFS or OS. Plasma cells from the combined samples formed clusters that could be differentiated by gene expression with each cluster having a unique combination of cells of varying PFS. Gene and protein expression differences were seen in PCs when samples were clustered by PFS suggesting a role for these molecules in disease progression. Finally, we identified gene expression changes in one patient between a diagnostic sample and relapse disease. Conclusions We present the first application of single cell multi-omics immune profiling in high-risk MM. Our results suggest that t(4;14) MM is a molecularly heterogeneous disease. That heterogeneity, coupled with our small sample size may explain the lack of correlation between gene or protein expression with clinical outcomes. Single cell analysis of larger cohorts is required to build on our findings.

Improved integration of single-cell transcriptome and surface protein expression by LinQ-View

SummaryMultimodal advances in single-cell sequencing have enabled the simultaneous quantification of cell surface protein expression alongside unbiased transcriptional profiling. Here, we present LinQ-View, a toolkit designed for multimodal single-cell data visualization and analysis. LinQ-View integrates transcriptional and cell surface protein expression profiling data to reveal more accurate cell heterogeneity and proposes a quantitative metric for cluster purity assessment. Through comparison with existing multimodal methods on multiple public CITE-seq datasets, we demonstrate that LinQ-View efficiently generates accurate cell clusters, especially in CITE-seq data with routine numbers of surface protein features, by preventing variations in a single surface protein feature from affecting results. Finally, we utilized this method to integrate single-cell transcriptional and protein expression data from SARS-CoV-2-infected patients, revealing antigen-specific B cell subsets after infection. Our results suggest LinQ-View could be helpful for multimodal analysis and purity assessment of CITE-seq datasets that target specific cell populations (e.g., B cells).

Abstract 3145: TRIPODD: a novel fluorescence imaging platform for in situ quantification of drug distribution and therapeutic response

Abstract Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells as well as signaling pathway cross talk. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this interaction network and identify therapeutic targets. Tyrosine kinase inhibitors (TKI) are a class of molecularly targeted therapeutics seeking to inhibit signaling pathways critical to sustaining proliferative signaling, resisting cell death, and other hallmarks of cancer. While tumors may initially respond to TKI therapy, disease progression is inevitable due to acquired resistance largely involving cellular signaling pathway reprogramming. With the ultimate goal of improved molecularly targeted therapeutic efficacy, we have developed a fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution), resulting in the only methodology capable of simultaneous quantification of single-cell drug target availability and protein expression with preserved tumor spatial context. TRIPODD combines intracellular paired agent imaging (iPAI) to quantify drug target interactions and oligonucleotide conjugated antibody cyclic immunofluorescence (cyCIF) to characterize proteomic response to therapy. Importantly, iPAI and cyCIF data is collected on the same tissue sections facilitating spatial registration of the two datasets. iPAI employs spectrally distinct, fluorescently labeled targeted and untargeted drug derivatives, which correct for untargeted uptake and facilitate quantitative in situ assessment of drug target engagement. cyCIF exploits in situ hybridization of complementary oligos for biomarker labeling, while oligo modifications facilitate signal removal for sequential rounds of fluorescent tagging. cyCIF is capable of generating multi-parametric images to quantify protein expression, distribution and phosphorylation. To date, we have quantified and evaluated our iPAI toolbox using a suite of functional assays, where in vitro screening confirmed biological functionality. Subsequent in vivo validation studies successfully performed ratiometric iPAI quantification after systemic iPAI probe administration, followed by sequential cyCIF imaging on the same xenograft tissue sections for single-cell quantification of drug target availability, EGFR pathway signaling and cell viability. TRIPODD will enable an improved mechanistic understanding of clinically-relevant treatment regimens through spatially resolved single-cell quantification of drug concentration and proteomic response to identify mechanisms of resistant subclonal population outgrowths driving resistance. Citation Format: Nathan P. McMahon, Allison Solanki, Jocelyn A. Jones, Lei G. Wang, Kenneth M. Tichauer, Kimberley S. Samkoe, Summer L. Gibbs. TRIPODD: a novel fluorescence imaging platform for in situ quantification of drug distribution and therapeutic response [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 3145.

Abstract 2259: Single-cell multi-omics analysis of SNV, CNV, and protein expression

Abstract Recent advancements in precision medicine, while highly promising, presents a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multi-omics analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights on cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri Platform enables multiplex-PCR based high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a new workflow is developed to detect protein expression in addition to DNA genotype in the same single cells. In this approach, cells are labeled with a pool of oligonucleotide-conjugated antibodies prior to loading the cells into the Tapestri Instrument. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. The number of protein targets can be in the range from 6 to over 40, which is beyond the limit for a single flow cytometry run. This method has been successfully performed on cell lines, fresh and frozen PBMCs, as well as clinical samples. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data clearly identified CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs. CD34hiCD11blo and CD34loCD11bhi subpopulations were also identified within the cells carrying the same pathogenic SNVs and CNVs. We believe that this novel multi-omics technology will facilitate new discoveries in the complex relationship between genotype and phenotype, enable a better understanding of disease biology, and subsequently improve the design of diagnostics and therapies. Citation Format: Aik Ooi, Dalia Dhingra, Adam Sciambi, Kate Thompson, Jacqueline Marin, Saurabh Parikh, Mani Manivannan, David Ruff. Single-cell multi-omics analysis of SNV, CNV, and protein expression [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 2259.

A combined approach for single-cell mRNA and intracellular protein expression analysis

Combined measurements of mRNA and protein expression in single cells enable in-depth analysis of cellular states. We present SPARC, an approach that combines single-cell RNA-sequencing with proximity extension essays to simultaneously measure global mRNA and 89 intracellular proteins in individual cells. We show that mRNA expression fails to accurately reflect protein abundance at the time of measurement, although the direction of changes is in agreement during neuronal differentiation. Moreover, protein levels of transcription factors better predict their downstream effects than do their corresponding transcripts. Finally, we highlight that protein expression variation is overall lower than mRNA variation, but relative protein variation does not reflect the mRNA level. Our results demonstrate that mRNA and protein measurements in single cells provide different and complementary information regarding cell states. SPARC presents a state-of-the-art co-profiling method that overcomes current limitations in throughput and protein localization, including removing the need for cell fixation.

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.

Introducing ADNP and SIRT1 as new partners regulating microtubules and histone methylation.

Activity-dependent neuroprotective protein (ADNP) is essential for brain formation and function. As such, de novo mutations in ADNP lead to the autistic ADNP syndrome and somatic ADNP mutations may drive Alzheimer's disease (AD) tauopathy. Sirtuin 1 (SIRT1) is positively associated with aging, the major risk for AD. Here, we revealed two key interaction sites for ADNP and SIRT1. One, at the microtubule end-binding protein (EB1 and EB3) Tau level, with EB1/EB3 serving as amplifiers for microtubule dynamics, synapse formation, axonal transport, and protection against tauopathy. Two, on the DNA/chromatin site, with yin yang 1, histone deacetylase 2, and ADNP, sharing a DNA binding motif and regulating SIRT1, ADNP, and EB1 (MAPRE1). This interaction was linked to sex- and age-dependent altered histone modification, associated with ADNP/SIRT1/WD repeat-containing protein 5, which mediates the assembly of histone modification complexes. Single-cell RNA and protein expression analyses as well as gene expression correlations placed SIRT1-ADNP and either MAPRE1 (EB1), MAPRE3 (EB3), or both in the same mouse and human cell; however, while MAPRE1 seemed to be similarly regulated to ADNP and SIRT1, MAPRE3 seemed to deviate. Finally, we demonstrated an extremely tight correlation for the gene transcripts described above, including related gene products. This correlation was specifically abolished in affected postmortem AD and Parkinson's disease brain select areas compared to matched controls, while being maintained in blood samples. Thus, we identified an ADNP-SIRT1 complex that may serve as a new target for the understanding of brain degeneration.

TRIPODD: a Novel Fluorescence Imaging Platform for In Situ Quantification of Drug Distribution and Therapeutic Response.

Personalized medicine has largely failed to produce curative therapies in advanced cancer patients. Evaluation of in situ drug target availability (DTA) concomitant with local protein expression is critical to an accurate assessment of therapeutic efficacy, but tools capable of both are currently lacking. We developed and optimized a fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution), resulting in the only methodology capable of simultaneous quantification of single-cell DTA and protein expression with preserved spatial context within a tumor. Using TRIPODD, we demonstrate the feasibility of combining two complementary fluorescence imaging techniques, intracellular paired agent imaging (iPAI) and cyclic immunofluorescence (cyCIF), conducted with oligonucleotide-conjugated antibodies (Ab-oligos) on tissue samples. We successfully performed sequential imaging on a single tissue section of iPAI to capture single-cell DTA and local protein expression heterogeneity using Ab-oligo cyCIF. Fluorescence imaging data acquisition was followed by spatial registration resulting in high dimensional data correlating DTA to protein expression at the single-cell level where uptake of a targeted probe alone was not well correlated to protein expression. Herein, we demonstrated the utility of TRIPODD as a powerful imaging platform capable of interpreting tumor heterogeneity for a mechanistic understanding of therapeutic response and resistance through quantification of drug target availability and proteomic response with preserved spatial context at single-cell resolution.

Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

AbstractHematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro–generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

A nearest-neighbor based nonparametric test for viral remodeling in heterogeneous single-cell proteomic data

An important problem in contemporary immunology studies based on single-cell protein expression data is to determine whether cellular expressions are remodeled postinfection by a pathogen. One natural approach for detecting such changes is to use nonparametric two-sample statistical tests. However, in single-cell studies direct application of these tests is often inadequate, because single-cell level expression data from processed uninfected populations often contain attributes of several latent subpopulations with highly heterogeneous characteristics. As a result, viruses often infect these different subpopulations at different rates, in which case the traditional nonparametric two-sample tests for checking similarity in distributions are no longer conservative. In this paper, we propose a new nonparametric method for Testing Remodeling under Heterogeneity (TRUH) that can accurately detect changes in the infected samples compared to possibly heterogeneous uninfected samples. Our testing framework is based on composite nulls and is designed to allow the null model to encompass the possibility that the infected samples, though unaltered by the virus, might be dominantly arising from underrepresented subpopulations in the baseline data. The TRUH statistic, which uses nearest neighbor projections of the infected samples into the baseline uninfected population, is calibrated using a novel bootstrap algorithm. We demonstrate the nonasymptotic performance of the test via simulation experiments and also derive the large sample limit of the test statistic which provides theoretical support toward consistent asymptotic calibration of the test. We use the TRUH statistic for studying remodeling in tonsillar T cells under different types of HIV infection and find that, unlike traditional tests which do not have any heterogeneity correction, TRUH based statistical inference conforms to the biologically validated immunological theories on HIV infection.

Precise determination of input-output mapping for multimodal gene circuits using data from transient transfection.

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.