Multimodal Profiling Research Articles

Abstract 984: Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis

Abstract Brain metastases are the most frequent malignancies in the brain and are associated with significant morbidity and mortality. Melanoma brain metastases (MBM) occur in most patients with advanced melanoma and are challenging to treat. Our understanding of the treatment-naïve landscape of MBM is still rudimentary, and there are no site-specific molecular therapies available. To gain comprehensive insights into the niche-specific biology of MBM, we performed multi-modal profiling of fresh and frozen samples using single-cell RNA-seq, single-cell TCR-seq, single-nuclei RNA-seq, and spatial transcriptional profiling. We evolved single-nucleus RNA-seq processing methods to enable profiling of minute amounts of archival, frozen specimens and compared data quality and structure between matched fresh and frozen MBM. We curated a treatment-naïve single-transcriptome atlas of MBM, collected either fresh samples over 1 year or profiled frozen samples dating back more than 15 years, and compared these samples to extracranial melanoma metastases (ECMM). In total, we profiled 25 samples with more than 114,000 transcriptomes. We identified more than 20 different cell types, including diverse tumor-infiltrating T-cell subsets and rare dendritic cell types, and tissue-specific cell types, such as activated microglia. Tumor cells in MBM showed an increase in copy number alterations (CNAs) compared to ECMM, which we validated using an external dataset of whole exome sequencing (WES) data including both MBM and ECMM. MBM-derived tumor cells show enrichment of genes involved in neuronal development and function, and site-specific metabolic programs (e.g., oxidative phosphorylation). Comparison with an external bulk RNA-seq dataset validated enriched key genes in MBM and ECMM as putative dependencies. We recovered cell-cell interactions between tumor and brain-resident cells involved in brain development, homeostasis, and disease. Similar to ECMM, the tumor microenvironment of MBM contained CD8+ T cells across a spectrum of differentiation, exhaustion and expansion, which was associated with loss of TCF7 expression and adoption of a TOX+ cell state. CD4+ T cells included T regulatory, T helper and T follicular-helper-like expression profiles. Plasma cells showed spatially localized expansion and limited heterogeneity. Myeloid cells largely adopted pro-tumorigenic cell states, including microglia, the brain-resident myeloid cells, which showed an activation trajectory characterized by expression of SPP1 (osteopontin). Spatial transcriptional analysis revealed restricted expression of antigen presentation genes with only a subset of these locations showing a type I interferon response. In summary, this work presents a multi-modal single-cell approach to dissect and compare the landscape of treatment-naïve MBM and ECMM. Citation Format: Johannes C. Melms, Jana Biermann, Amit Dipak Amin, Yiping Wang, Somnath Tagore, Massimo Andreatta, Ajay Nair, Meri Rogava, Patricia Ho, Lindsay A. Caprio, Zachary H. Walsh, Shivem Shah, Daniel H. Vacarro, Blake Caldwell, Adrienne M. Luoma, Joseph Driver, Matthew Ingham, Suthee Rapisuwon, Jennifer Wargo, Craig L. Slinguff, Evan Z. Macosco, Fei Chen, Richard Carvajal, Michael B. Atkins, Michael A. Davies, Elham Azizi, Santiago J. Carmona, Hanina Hibshoosh, Peter D. Canoll, Jeffrey N. Bruce, Wenya L. Bi, Gary K. Schwartz, Benjamin Izar. Dissecting the ecosystem of treatment-naïve melanoma brain metastasis using multi-modal single-cell analysis [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 984.

Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer.

Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC. We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor. We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers. We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.

Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors.

e23518 Background: Single-cell RNA-seq is an enabling technology that may inform the molecular underpinnings of drug response and resistance in patient biopsies. This method is difficult to implement in the study of rare diseases such as sarcomas due to specimen requirements and technical limitations. Methods: Here, we evolved novel methods that we recently reported in melanoma ( Wang, Fan, et al., bioRxiv, 2022), which enable single-nucleus RNA, T cell receptor (snRNA/TCR)-seq, and pool-matched whole-genome sequencing (WGS) from archival, frozen sarcoma tissue. Results: This enabled profiling of 75,716 cells and 788 matched TCR clonotypes from six patients with intimal sarcoma (INS) and undifferentiated pleomorphic sarcomas (UPS), including two matched pair samples from pre/post-immune checkpoint inhibitor (ICI). Our analysis revealed substantial transcriptional cancer cell heterogeneity driven by varying copy number alterations (CNAs). In one patient with INS with a complete response to ICI followed by an isolated recurrence, we identified a rare cancer cell clone defined by CNA (confirmed with WGS) and resulting transcriptional outputs that pre-existed and emerged during resistance. Furthermore, in a patient with UPS with intrinsic resistance to ICI, we find adequate T cell clonal expansion and activation, suggesting appropriate T cell response to ICI dampened by intrinsic mechanisms of ICI resistance within the cancer cells. Non-negative matrix factorization (NMF) analysis identified cell states associated with either intrinsic or adaptive resistance to ICI that was distinct from resistance to doxorubicin. These observations are consistent with those previously reported from sequential biopsies obtained from KEYNOTE-001 in metastatic melanoma ( Wang, Fan, et al., bioRxiv, 2022), which also revealed emergence of pre-existing populations of resistant clones defined by their underlying aneuploidy patterns. Conclusions: Together, these results demonstrate feasibility of implementing single-cell genomics from archival tissue to study sarcoma and propel our understanding of drug resistance. Conceptually, this work suggests that large-scale CNAs may drive cell sub-populations associated with ICI resistance in sarcoma and in other diseases.

Multi-modal immune profiling of mucinous ovarian carcinoma: analysis from the Ovarian Tumor Tissue Analysis/Multidisciplinary Ovarian Cancer Outcomes Group consortia

Abstract Mucinous ovarian carcinoma (MOC) is a rare histotype of ovarian cancer. Women with advanced disease (FIGO stage III/IV) have very poor outcomes, with 5-year survival of <20%. Little is known about the anti-tumor immune response in MOC, limiting potential immunotherapeutic options for patients. Immune data from 3 platforms were analysed. A NanoString mRNA expression panel with 39 immune-related genes (n=60 MOC, n=4198 other ovarian histotypes), a NanoString mRNA plexset with PD-1, PD-L1 (n=241 MOC, n=115 upper and lower gastrointestinal (GI) cancers), and 2 multicolor immunofluorescence (mcIF) panels (n=121 MOC) for CD68, PD-L1, PD-1, CD8 and FOXP3. Data overlapped all platforms for 22 MOC. Immune marker expression by ovarian cancer histotype was measured. Uni- and multivariable Cox Proportional Hazards assessed overall survival (OS) within MOC. Hierarchical clustering looked for subgroups of patients based on co-expression of immune genes. Compared to more common high-grade serous ovarian cancer (HGSC) and GI cancers, MOC were less immunologically active. High IGHM expression was associated with poorer OS (HR 1.31 (95% CI 1.02–1.69), p=0.033). High tumor densities of FOXP3 and PD-1 positive cells conferred poorer OS in a model with age, stage and site; however, these and PD-L1 and CD68 were associated with grade. The 39 gene NanoString and mcIF datasets revealed 4 clusters, roughly translating to immune cold, moderate and hot (groups 3,4). mcIF analysis revealed a subset with PD-L1 expression in the absence of T-regulatory cell infiltrates. This study provides novel insights into the MOC immune landscape. Despite relatively low immune activity overall, immunotherapy could be explored for a subset of patients with MOC. Supported by NSW Ministry of Health and UNSW Sydney, through the NSW Health PhD Scholarship Program 2017-2022; Translational Cancer Research Network top-up scholarship 2021

Barcode Enabled Antigen Mapping (BEAM) enables next-generation systems immunology analysis of the post-COVID-19 immune landscape

Abstract A more complete understanding of immune responses to disease requires consideration of multiple different cell types across both the innate and adaptive branches of the immune system along with the detection of many different analytes. We used Barcode Enabled Antigen Mapping (BEAM) and Immune Profiling technology to perform simultaneous multimodal profiling at single cell resolution in hundreds of thousands of peripheral blood mononuclear cells (PBMCs) from a human donor following recovery from COVID-19. In addition to measuring gene and protein expression, we generated full-length, paired sequences of the rearranged T- and B-cell receptors while also screening their specificity for a wide range of antigens from SARS-CoV-2 and other viral pathogens. These data provide insights into the entirety of the immune landscape after recovery from acute viral disease. The scale and throughput of our experiments gave us high-resolution data from all cell types from the innate and adaptive immune systems. We identified antigen-specific clones of both B and T lymphocytes, with the high cellular throughput enabling detection of rare clones. Analysis of all PBMC cell types allowed us to place these antigen-specific clones within the overall transcriptional landscape of the post-viral immune system. Experiments such as these will underpin new systems immunology approaches and will continue to reveal the complex interplay between the components of the immune system. We envisage that these methods will be valuable in the analysis of the immune response to vaccination, infectious disease, cancer, allergy, autoimmune conditions, and ageing that can potentially lead to the development of novel diagnostic and therapeutic approaches.

Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control

SummaryMycobacterium tuberculosis lung infection results in a complex multicellular structure: the granuloma. In some granulomas, immune activity promotes bacterial clearance, but in others, bacteria persist and grow. We identified correlates of bacterial control in cynomolgus macaque lung granulomas by co-registering longitudinal positron emission tomography and computed tomography imaging, single-cell RNA sequencing, and measures of bacterial clearance. Bacterial persistence occurred in granulomas enriched for mast, endothelial, fibroblast, and plasma cells, signaling amongst themselves via type 2 immunity and wound-healing pathways. Granulomas that drove bacterial control were characterized by cellular ecosystems enriched for type 1-type 17, stem-like, and cytotoxic T cells engaged in pro-inflammatory signaling networks involving diverse cell populations. Granulomas that arose later in infection displayed functional characteristics of restrictive granulomas and were more capable of killing Mtb. Our results define the complex multicellular ecosystems underlying (lack of) granuloma resolution and highlight host immune targets that can be leveraged to develop new vaccine and therapeutic strategies for TB.

Normalizing and denoising protein expression data from droplet-based single cell profiling

Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called “dsb” (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at “dsb [https://cran.r-project.org/package=dsb]”.

Multi-modal profiling of human fetal liver hematopoietic stem cells reveals the molecular signature of engraftment

The human hematopoietic stem cell harbors remarkable regenerative potential that can be harnessed therapeutically. During early development, hematopoietic stem cells in the fetal liver undergo active expansion while simultaneously retaining robust engraftment capacity, yet the underlying molecular program responsible for their efficient engraftment remains unclear. Here, we profile 26,407 fetal liver cells at both the transcriptional and protein level including ~7,000 highly enriched and functional fetal liver hematopoietic stem cells to establish a detailed molecular signature of engraftment potential. Integration of transcript and linked cell surface marker expression reveals a generalizable signature defining functional fetal liver hematopoietic stem cells and allows for the stratification of enrichment strategies with high translational potential. More precisely, our integrated analysis identifies CD201 (endothelial protein C receptor (EPCR), encoded by PROCR) as a marker that can specifically enrich for engraftment potential. This comprehensive, multi-modal profiling of engraftment capacity connects a critical biological function at a key developmental timepoint with its underlying molecular drivers. As such, it serves as a useful resource for the field and forms the basis for further biological exploration of strategies to retain the engraftment potential of hematopoietic stem cells ex vivo or induce this potential during in vitro hematopoietic stem cell generation.

Ulcerative colitis is characterized by a plasmablast-skewed humoral response associated with disease activity.

B cells, which are critical for intestinal homeostasis, remain understudied in ulcerative colitis (UC). In this study, we recruited three cohorts of patients with UC (primary cohort, n = 145; validation cohort 1, n = 664; and validation cohort 2, n = 143) to comprehensively define the landscape of B cells during UC-associated intestinal inflammation. Using single-cell RNA sequencing, single-cell IgH gene sequencing and protein-level validation, we mapped the compositional, transcriptional and clonotypic landscape of mucosal and circulating B cells. We found major perturbations within the mucosal B cell compartment, including an expansion of naive B cells and IgG+ plasma cells with curtailed diversity and maturation. Furthermore, we isolated an auto-reactive plasma cell clone targeting integrin αvβ6 from inflamed UC intestines. We also identified a subset of intestinal CXCL13-expressing TFH-like T peripheral helper cells that were associated with the pathogenic B cell response. Finally, across all three cohorts, we confirmed that changes in intestinal humoral immunity are reflected in circulation by the expansion of gut-homing plasmablasts that correlates with disease activity and predicts disease complications. Our data demonstrate a highly dysregulated B cell response in UC and highlight a potential role of B cells in disease pathogenesis.

Multimodal profiling of the transcriptional regulatory landscape of the developing mouse cortex identifies Neurog2 as a key epigenome remodeler

How multiple epigenetic layers and transcription factors (TFs) interact to facilitate brain development is largely unknown. Here, to systematically map the regulatory landscape of neural differentiation in the mouse neocortex, we profiled gene expression and chromatin accessibility in single cells and integrated these data with measurements of enhancer activity, DNA methylation and three-dimensional genome architecture in purified cell populations. This allowed us to identify thousands of new enhancers, their predicted target genes and the temporal relationships between enhancer activation, epigenome remodeling and gene expression. We characterize specific neuronal transcription factors associated with extensive and frequently coordinated changes across multiple epigenetic modalities. In addition, we functionally demonstrate a new role for Neurog2 in directly mediating enhancer activity, DNA demethylation, increasing chromatin accessibility and facilitating chromatin looping in vivo. Our work provides a global view of the gene regulatory logic of lineage specification in the cerebral cortex.

Multi-modal Profiling of the Extracellular Matrix of Human Fallopian Tubes and Serous Tubal Intraepithelial Carcinomas.

Recent evidence supports the fimbriae of the fallopian tube as one origin site for high-grade serous ovarian cancer (HGSOC). The progression of many solid tumors is accompanied by changes in the microenvironment, including alterations of the extracellular matrix (ECM). Therefore, we sought to determine the ECM composition of the benign fallopian tube and changes associated with serous tubal intraepithelial carcinomas (STICs), precursors of HGSOC. The ECM composition of benign human fallopian tube was first defined from a meta-analysis of published proteomic datasets that identified 190 ECM proteins. We then conducted de novo proteomics using ECM enrichment and identified 88 proteins, 7 of which were not identified in prior studies (COL2A1, COL4A5, COL16A1, elastin, LAMA5, annexin A2, and PAI1). To enable future in vitro studies, we investigated the levels and localization of ECM components included in tissue-engineered models (type I, III, and IV collagens, fibronectin, laminin, versican, perlecan, and hyaluronic acid) using multispectral immunohistochemical staining of fimbriae from patients with benign conditions or STICs. Quantification revealed an increase in stromal fibronectin and a decrease in epithelial versican in STICs. Our results provide an in-depth picture of the ECM in the benign fallopian tube and identified ECM changes that accompany STIC formation. (J Histochem Cytochem XX: XXX-XXX, XXXX).

A Multimodal Author Profiling System for Tweets

The rising usage of social media has motivated to invent different methodologies of anonymous writing, which leads to an increase in malicious and suspicious activities. This anonymity has created difficulty in finding the suspect. Author profiling deals with the characterization of an author through some key attributes such as gender, age, language, dialect region variety, personality, and so on. Identifying the gender of the author of a suspect document is a salient task of author-profiling. The linguistic profile of a user can help in determining his/her demographics. Different social media platforms, such as Twitter, Facebook, and Instagram, are used regularly by users for sharing their daily life activities. Moreover, users often post images along with text on different social media platforms; thus, the usage of multimodal information is very common nowadays. In this article, the task of automatic gender prediction from multimodal Twitter data is posed as a classification problem and an efficient multimodal neural framework is proposed for solving this. The popularly used BERT_base is utilized for learning the encoded representation for the text part of the tweet, and recently introduced EfficientNet is used for extracting the features from images. Finally, a direct product-based fusion strategy is applied for fusing the text and image representations, followed by a fully connected layer for predicting the gender of a Twitter user. Plagiarism detection authorship analysis near end duplicate detection (PAN)-2018 author profiling data are used for evaluating the performance of our proposed approach. Our proposed model achieved accuracies of 82.05%, 86.22%, and 89.53% for pure-image, pure-text, and multimodal setting, respectively; outperforming the previous state-of-the-art works in all the cases. Moreover, a deep analysis is carried out to interpret the produced results; different words that serve as clues for gender classification are identified characterizing different gender classes. The supplementary file and the source codes for the proposed approach are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chanchalIITP/GenderTCSS</uri> .

Precision medicine in secondary prevention of ischemic stroke: how may blood-based biomarkers help in clinical routine? An expert opinion.

One in eight patients unfortunately suffers a new stroke within 5 years of their first stroke, even today. Research in precision medicine could lead to a more individualized treatment allocation, possibly achieving lower recurrence rates of ischemic stroke. In this narrative review, we aim to discuss potential clinical implementation of several promising candidate blood biomarkers. We discuss specifically some promising blood-based biomarkers, which may improve the identification of underlying causes as well as risk stratification of patients according to their specific cerebrovascular risk factor pattern. Multimodal profiling of ischemic stroke patients by means of blood biomarkers, in addition to established clinical and neuroradiological data, may allow in the future a refinement of decision algorithms for treatment allocation in secondary ischemic stroke prevention.

Diagnostic Utility of Multimodal Genomic Profiling for Molecular Classification and MRD Assessment in Adult B-Cell Acute Lymphoblastic Leukemia

Genomic markers define molecular subtypes and measurable residual disease (MRD) targets in B-cell acute lymphoblastic leukemia/lymphoma (B-ALL) and are essential determinants of treatment. Current diagnostic approaches typically involve serial multi-step testing utilizing conventional cytogenetics (CC)/FISH and molecular genetic (RT-qPCR, MLPA, clonality PCR, NGS panel) techniques which are time and sample consuming and ultimately may not adequately identify genomically complex B-ALL subtypes. In contrast, single-step comprehensive genomic profiling by whole genome and whole transcriptome sequencing (WGS/WTS) may be more efficient for the molecular classification of established and newly described entities which are of increasing therapeutic relevance. We have instituted a multimodal platform for molecular testing in B-ALL performing WGS/WTS in parallel with deep NGS-based immunoglobulin (IG) rearrangement MRD and exploratory DNA-breakpoint based MRD assays. We aimed to determine the utility of this approach for subtype classification compared to a standard-of-care diagnostic approach of CC/FISH testing.Forty-two consecutive adult patients underwent both standard-of-care diagnostic testing and WGS/WTS. 20/42 (48%) patients had an abnormal CC/FISH result supporting classification into recognized molecular subgroups. WGS/WTS assessment incorporating somatic coding and non-coding mutations, structural variants, fusions, copy number abnormalities and gene expression subtype prediction (ALLSorts, https://github.com/Oshlack/ALLSorts) was performed with concordant results in all 20 patients. 16/22 patients that were unclassified by CC/FISH were successfully reclassified by WGS/WTS including subtypes enriched for cryptic rearrangements (Ph-like, DUX4, MEF2D) and groups characterized by heterogeneous genomic alterations or a distinctive gene expression signature (PAX5alt, ZEB2/CEBP). A low hypodiploid karyotype was observed in two cases with an apparently normal karyotype by CC. The six patients who remained without a subtype defining driver genetic alteration after comprehensive testing frequently harbored novel IGH translocations or a Ph-like expression signature but without a described fusion.In order to understand the relative contribution from WGS versus WTS, analysis of 36 patients was performed using a truth classification. WGS and WTS produced equivalent classifications for 22 cases. Two cases were based solely on WGS findings (iAMP21 and ZEB2/CEBP) and three cases were based solely on WTS findings (DUX4). Importantly the combination of both WGS and WTS was critical to correctly classify nine cases (Ph-like and PAX5alt).MRD was assessed by a sensitive NGS assay to IG rearrangements (Adaptive Biotechnologies) and by quantitative probe-based droplet digital PCR (ddPCR) assays designed to structural rearrangement DNA breakpoints from genome data (analytical sensitivity 10 -4). Patient-specific ddPCR assays were designed to eight structural variants (KMT2A and IGH translocations, and IKZF1 deletions) in seven patients and assessed in 36 remission samples with parallel testing by multiparametric flow cytometry (MFC). Concordant MFC and ddPCR was observed in 30/36 samples (19 MRD pos, 11 MRD neg). Discordances included two MRD pos by MFC-only and four MRD pos by ddPCR-only; the latter often occurring in the setting of antigen directed therapy or in ALL with a less informative immunophenotype, demonstrating the additional utility of non-MFC based MRD assessment in specific clinical settings. 27/42 patients in our cohort had ≥1 genomic structural rearrangement identified by WGS that could be used for patient-specific MRD monitoring to complement existing MRD assessment.In conclusion WGS/WTS provided a molecular subtype classification in 86% of our cohort compared to 48% by standard-of-care diagnostic testing highlighting that CC/FISH alone is inadequate for contemporary molecular classification of B-ALL, which may have implications for treatment decisions. Importantly, the combination of WGS and WTS was superior to WGS-only or WTS-only for correct molecular subtype assignment. This approach has the potential to improve risk assessment in adult B-ALL and the routine feasibility, improvement in clinical outcomes and health economic impact warrant further assessment. DisclosuresBajel: Abbvie, Amgen, Novartis, Pfizer: Honoraria; Amgen: Speakers Bureau. Dickinson: Janssen: Consultancy, Honoraria; Amgen: Honoraria; Celgene: Research Funding; Gilead Sciences: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding, Speakers Bureau; Takeda: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Other: travel, accommodation, expenses, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau. Tiong: Servier: Consultancy, Speakers Bureau; Amgen: Speakers Bureau; Pfizer: Consultancy.

SCITO-seq: single-cell combinatorial indexed cytometry sequencing.

The development of DNA-barcoded antibodies to tag cell surface molecules has enabled the use of droplet-based single-cell sequencing (dsc-seq) to profile protein abundances from thousands of cells simultaneously. As compared to flow and mass cytometry, the high per cell cost of current dsc-seq-based workflows precludes their use in clinical applications and large-scale pooled screens. Here, we introduce SCITO-seq, a workflow that uses splint oligonucleotides (oligos) to enable combinatorially indexed dsc-seq of DNA-barcoded antibodies from over 105 cells per reaction using commercial microfluidics. By encoding sample barcodes into splint oligos, we demonstrate that multiplexed SCITO-seq produces reproducible estimates of cellular composition and surface protein expression comparable to those from mass cytometry. We further demonstrate two modified splint oligo designs that extend SCITO-seq to achieve compatibility with commercial DNA-barcoded antibodies and simultaneous expression profiling of the transcriptome and surface proteins from the same cell. These results demonstrate SCITO-seq as a flexible and ultra-high-throughput platform for sequencing-based single-cell protein and multimodal profiling.

Modeling alcohol-associated liver disease in a human Liver-Chip.

SUMMARYAlcohol-associated liver disease (ALD) is a global health issue and leads to progressive liver injury, comorbidities, and increased mortality. Human-relevant preclinical models of ALD are urgently needed. Here, we leverage a triculture human Liver-Chip with biomimetic hepatic sinusoids and bile canaliculi to model ALD employing human-relevant blood alcohol concentrations (BACs) and multimodal profiling of clinically relevant endpoints. Our Liver-Chip recapitulates established ALD markers in response to 48 h of exposure to ethanol, including lipid accumulation and oxidative stress, in a concentration-dependent manner and supports the study of secondary insults, such as high blood endotoxin levels. We show that remodeling of the bile canalicular network can provide an in vitro quantitative readout of alcoholic liver toxicity. In summary, we report the development of a human ALD Liver-Chip as a powerful platform for modeling alcohol-induced liver injury with the potential for direct translation to clinical research and evaluation of patient-specific responses.

Tumor-Naïve Multimodal Profiling of Circulating Tumor DNA in Head and Neck Squamous Cell Carcinoma.

Circulating tumor DNA (ctDNA) enables personalized treatment strategies in oncology by providing a noninvasive source of clinical biomarkers. In patients with low ctDNA abundance, tumor-naïve methods are needed to facilitate clinical implementation. Here, using locoregionally confined head and neck squamous cell carcinoma (HNSCC) as an example, we demonstrate tumor-naïve detection of ctDNA by simultaneous profiling of mutations and methylation. We conducted CAncer Personalized Profiling by deep Sequencing (CAPP-seq) and cell-free Methylated DNA ImmunoPrecipitation and high-throughput sequencing (cfMeDIP-seq) for detection of ctDNA-derived somatic mutations and aberrant methylation, respectively. We analyzed 77 plasma samples from 30 patients with stage I-IVA human papillomavirus-negative HNSCC as well as plasma samples from 20 risk-matched healthy controls. In addition, we analyzed leukocytes from patients and controls. CAPP-seq identified mutations in 20 of 30 patients at frequencies similar to that of The Tumor Genome Atlas (TCGA). Differential methylation analysis of cfMeDIP-seq profiles identified 941 ctDNA-derived hypermethylated regions enriched for CpG islands and HNSCC-specific methylation patterns. Both methods demonstrated an association between ctDNA abundance and shorter fragment lengths. In addition, mutation- and methylation-based ctDNA abundance was highly correlated (r > 0.85). Patients with detectable pretreatment ctDNA by both methods demonstrated significantly worse overall survival (HR = 7.5; P = 0.025) independent of clinical stage, with lack of ctDNA clearance post-treatment strongly correlating with recurrence. We further leveraged cfMeDIP-seq profiles to validate a prognostic signature identified from TCGA samples. Tumor-naïve detection of ctDNA by multimodal profiling may facilitate biomarker discovery and clinical use in low ctDNA abundance applications.

Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells.

Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.

Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease.

Multimodal T cell profiling can enable more precise characterization of elusive cell states underlying disease. Here, we integrated single-cell RNA and surface protein data from 500,089 memory T cells to define 31 cell states from 259 individuals in a Peruvian tuberculosis (TB) progression cohort. At immune steady state >4 years after infection and disease resolution, we found that, after accounting for significant effects of age, sex, season, and genetic ancestry on T cell composition, a polyfunctional Th17-like effector state was reduced in abundance and function in individuals who previously progressed from Mycobacterium tuberculosis (M.tb) infection to active TB disease. These cells are capable of responding to M.tb peptides. Deconvoluting this state—uniquely identifiable with multimodal analysis—from public data demonstrated that its depletion may precede and persist beyond active disease. Our study demonstrates the power of integrative multimodal single-cell profiling to define cell states relevant to disease and other traits.

Mitigating myopia in tuberculosis.

C-12, a cluster of CD4+ TH17-like cells, defined by unbiased multimodal profiling of memory T cells, is significantly reduced in patients who have recovered from TB (progressors) as compared to those who were infected but did not develop the disease (non-progressors).