Single-cell Multiomics Research Articles

Extrapolated Cross-Validation for Randomized Ensembles

Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This article introduces a cross-validation method, Extrapolated Cross-Validation (ECV), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields δ-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and K-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique. Supplementary materials for this article are available online.

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics.

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.

Dissecting Subtype-Specific Tumor-Time Interactions and Underlying Hidden Drivers in Pediatric Acute Myeloid Leukemia Via Single-Cell Multi-Omics

Introduction: Pediatric acute myeloid leukemia (AML) is a heterogeneous hematological malignancy characterized by various chromosomal abnormalities and somatic mutations. Investigating the origin of tumorigenesis and deciphering the complex interplay between AML cells and their surrounding tumor immune microenvironment (TIME) is crucial to understand the underlying mechanisms driving disease progression and response to therapy. Recent advancements in single-cell multi-omics technologies enabled us to characterize the transcriptional (GEX) and epigenetic (ATAC) landscapes of both tumors and TIMEs. Leveraging our in-house algorithm, scMINER (single-cell Mutual Information-based Network Engineering Ranger, Ding et al., 2023), this study aims to build a comprehensive cell type atlas in diverse pediatric AML bone marrow samples and construct intra- and inter-cellular networks, enabling the identification of hidden drivers that shape subtype-specific AML-TIME interactions. Methods: Using CD45 and CD33 as selective markers by flow cytometry, we have successfully isolated blasts and enriched for T cell and B cell populations for 10X single-cell multiome profiling across AML molecular subtypes, including UBTF-TD (n=1), NPM1 (n=1), and KMT2A rearrangement (n=1). We interrogated the GEX data using scMINER, including two key steps: (i) mutual information-based clustering analysis (MICA) and (ii) mutual information-based network inference engine (MINIE). MICA characterizes the intrinsic nonlinear similarity of gene expression distributions among cells, enabling a high resolution of clusters. MINIE reverse-engineers cluster-specific intracellular gene networks and infers protein activity of transcription factors (TF) or signaling factors (SIG) drivers based on the expression of its predicted regulon targets in the corresponding cluster. The activity profiles overcome the dropout effects of single-cell RNA-seq data and reflect protein activities across all cells. ArchR is used to analyze ATAC data. CellChat is used to construct and evaluate intercellular networks. Results: AML cells from three samples were divided into four major clusters, and we labeled them as NPM1, KMT2A, UBTF_one, and UBTF_two based on sample subtypes (Fig.1A). MICA managed to obtain distinct AML cell sub-clusters. Comparison with single-cell profiles of normal hematopoietic cells and annotation with marker genes revealed their correspondence with different myeloid developmental stages. Notably, the KMT2A cluster shared a high similarity in cell type proportion with the UBTF_two cluster, and the NPM1 cluster is closer to the UBTF_one cluster. This result demonstrated significant tumor heterogeneity within and across AML molecular subtypes. With the help of intracellular gene networks generated by MINIE, we identified multiple subtype-specific hidden drivers by differential activity analysis. For example, KMT2A and UBTF_two clusters exhibited elevated SAMHD1 levels, consistent with previous GSE data (Zhang et al., 2022). Chromatin accessibility analysis of ATAC data showed increased peaks near SAMHD1 in KMT2A and UBTF_two clusters, further supporting this observation. IRF8 showed a similar expression pattern to SAMHD1, suggesting its context-dependent role across AML subtypes. Accurate annotation of myeloid and immune cells greatly enriches the preciseness of the AML and TIME interaction patterns, which vary across AML molecular subtypes (Fig.1B). Interestingly, cells originated from KMT2A cluster exhibited similar interaction patterns to UBTF_two, while NPM1 and UBTF_one were grouped. This result indicated that different AML molecular subtypes may partially utilize the same mechanisms of tumor-TIME interaction, resulting in similar tumor phenotypes. Conclusions: Accurate clustering and well-defined annotation of myeloid and immune cells enabled us to build a comprehensive cell type atlas in AML samples. The subtype-specific network and the following analysis identified candidate hidden drivers that possibly contribute to the heterogeneity of tumor populations. AML subtypes sharing the same hidden drivers have similar cell type proportions and interaction patterns with TIMEs. This observation implies that AML subtypes share cell type-specific dependencies, which can be therapeutic targets to overcome the refractoriness posed by tumor heterogeneity.

Single Cell Multi Omics and Spatial Analysis Reveals Plasmablast Signature Malignant Cells As a Key of Intratumor Heterogeneity in Primary Central Nervous System Lymphoma

Background: Primary central nervous system lymphoma (PCNSL) constitutes approximately 5% of extranodal lymphomas. More than 90% of PCNSL are diagnosed as diffuse large B-cell lymphomas (DLBCL), mainly classified as the activated B cell (ABC) subtype. Recent genetic analyses have identified key molecular drivers of PCNSL, including MYD88, CARD11 and CD79B. However, molecular analyses using bulk samples have provided limited insights into the heterogeneity in lymphoma cells of PCNSL. To improve our understanding in the biology of PCNSL, we conducted a multimodal single-cell analyses and integrated single-cell and spatial analyses to characterize the molecular properties of PCNSL tumors. Methods:Our study included seven PCNSL patients and two patients with reactive lymph nodes (RLN). We performed Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) combined with B-cell and T-cell receptor (BCR and TCR) analysis on samples from 3 PCNSL and 2 RLN patients, while the remaining PCNSL patients' samples were subjected to single-cell transcriptomic sequencing. Additionally, six formalin-fixed and paraffin-embedded (FFPE) tissues from the patients analyzed in the single-cell sequencing (except for one patient) were performed with GeoMx digital spatial profiling (DSP). Result: To ensure data quality, we filtered out low-quality cells and doublets, resulting in a dataset of 41,556 single cells. B cells represented the most abundant cluster, followed by T cells and monocytes. Copy number alterations (CNAs) data derived from InferCNV analysis showed distinct CNAs patterns in lymphoma cells of each patient. UMAP plot projection analysis revealed the presence of a distinct subtype of malignant cells within the B-cell population, which was characterized as high expression of plasmablast (PBL)-associated surface proteins, including SLAMF7, CD39, and CD38. Enrichment analysis based on germinal center (GC) gene expression signatures (Holmes AB et al. J Exp Med. 2020) revealed a broader spectrum of B-cell differentiation states in malignant B cells. Single-cell BCR sequencing further demonstrated that different GC signature state cells harbored the same BCR repertoire within each patient, suggesting a common origin of malignant cells. Non-negative matrix factorization (NMF) analysis confirmed the enrichment of PBL signature scores in the distinct malignant cell population observed in the UMAP plot, consistent with DPE analysis (Figure A). Interestingly, the proportion of PBL signature cells varied among patients. Signaling interaction analysis highlighted CD70-CD27 and SPP1-CD44 pathways as enriched in tumor cells. PBL signature cells received TNFSF13B and TNFSF13 signaling through TNFRSF17 (BCMA), whereas other tumor cells expressed TNFRSF13C (BAFF-R) and received TNFSF13B signaling alone. We further analyzed two public bulk RNA-seq data (Bruno A et al. Neuro Oncol 2018, n = 6; Radke J et al. Nat Commun 2022, n=38) in PCNSL to decompose tumor components. Tumor PBL components were identified in most PCNSL cases, although several cases exhibited a low presence of tumor PBL population. Of note, analysis including these public datasets with our single-cell cohort showed that PCNSL samples with high tumor PBL proportion had unfavorable prognosis . Additionally, we performed DSP to integrate the single-cell and spatial multi-omics analyses. We deconvoluted the cell composition by using transcriptomic data derived from each region of interest (ROI) and found that the cell proportions in ROI were consistent with the corresponding single-cell results. However, there were differential profiles of cell composition according to patients rather than ROIs, suggesting that PCNSL tumors may display spatial homogeneity (Figure B). Conclusion: Through the application of single-cell and spatial transcriptomic analysis, we identified a distinct subtype expressing the PBL signature gene. These findings have important therapeutic implications and provide new insights into the mechanisms of relapse in PCNSL.

Single-Cell Multi-Omics Reveals Type-2 Functionality in Maintaining CAR T Cell Longevity Associated with 8-Year Leukemia Remission

Despite a high response rate in chimeric antigen receptor (CAR) T therapy for acute lymphocytic leukemia (ALL), ~50% of patients relapse within the first year, representing an urgent question to address in the next stage of cellular immunotherapy. The pioneering clinical trials conducted at Upenn/CHOP provide a unique opportunity to examine the molecular determinants of ultra-long CAR T persistence. We performed single-cell multi-omics profiling of ~700k pre-infusion CD19-targeted 4-BB CAR T cells from 82 pediatric ALL patients and 6 healthy donors. Patient demographics were collected between Sep-2012 to July-2022. To uncover the hallmarks of CAR T longevity, we correlated the single-cell atlas with the duration of B-cell aplasia (BCA), a widely used pharmacodynamic measurement of CAR T persistence, and classified all the patients into 5 groups (Table 1). Five patients in our cohort have exhibited a median BCA duration of 8.4 years (BCA-L group) and another 11 patients continue to have BCA with a median time of 5.1 years (BCA-O group). To remove potential confounding variables between trial design, 42 patients including the 5 BCA-L were analyzed as the Discovery Cohort, while the other 40 patients including the 11 BCA-O were used as Validation Cohort. Our analysis of CD19-specific stimulated CAR T cells from the Discovery Cohort revealed a prominent role of type-1 function, which was highly represented but had no discernible correlation with long-term persistence. Unexpectedly, we identified that elevated type-2 functionality was significantly associated with BCA-L patients maintaining CAR T persistence of 8.4 years, and this signature was robustly presented in the BCA-O patients in our Validation Cohort. Higher type-2 cytokine level of long-term persistent CAR T cells was independently validated using both flow cytometry and multiplexed secretomic assay. Through ligand-receptor interaction analysis, type-2 cytokines were found to regulate a cluster of Tim-3+terminal effector cells showing over activation of cytotoxicity, impaired immune function, elevated exhaustion signature and diminished proliferative potentiality, and CAR T cells from BCA-L patients showed reduced dysfunctional hallmarks. We next conducted in vitro functional studies to assess the impact of adding type-2 cytokine (IL-4) during CAR-specific activation on CAR T fitness. The addition of 10ng/mL IL-4 significantly boosted the proliferation and mitigated dysfunction of CAR T cells from 6 patients in the BCA2 group that only mediated short-term response (~3 months) and had negligible impact on their type-1 functionality. Remarkably, the functional profile of short-term BCA2 CAR T cells after IL-4 supplementation exhibited a comparable pattern to that of long-term BCA-L CAR T cells, particularly in terms of type-2 pathway, oxidative phosphorylation metabolism, PI3K/AKT signaling, mTOR signaling, and cell cycle regulation. To investigate the response of the host following CAR T infusion, we performed a comprehensive proteomic profiling of post-infusion serum proteins in a total of 41 patients. Baseline measurements (-2 to 0 days before CAR T infusion) showed a significantly higher level of type-2 cytokines in long-term patients. From the rapid expansion period (day 6-8) until 11 days post-infusion, we consistently observed significantly higher levels of circulating type-2 cytokines in 5-year or 8-year relapse-free responders. Conversely, no difference was observed in type-1 cytokines. In a leukemic NSG mouse model, both type-2 high and low CAR T cells were highly effective in reducing tumor burden in the first two weeks. However, type-2 high CAR T had significantly superior expansion, and the absolute CAR+ cell counts in peripheral blood were 10-fold higher than type-2 low CAR T at day 8 and day 12. To mimic tumor cell relapse, a second dose of tumor cells were injected into mice on day 17. Notably, type-2 high CAR T cells demonstrated a potent capability to elicit recall responses upon leukemia rechallenge, significantly prolonging the survival of tumor-bearing mice compared to type-2 low CAR T. The superior performance could be associated with their decreased exhaustion and increased memory phenotype. Our findings provide key insights into the mediators of CAR T longevity and suggest a potential therapeutic strategy to maintain long-term remission by enhancing type-2 functionality in CAR T cells.

Multiomic Single-Cell Sequencing May Distinguish BCR-ABL-Mutated Acute Myeloid Leukemia (AML) from Blast Crisis Chronic Myelogenous Leukemia (BC-CML)

Introduction The existence of BCR-ABL+ AML as an entity distinct from BC-CML is controversial. The WHO recently recognized BCR-ABL+ AML, but with the caveat that it is poorly characterized (Khoury, 2022). Clinically, BCR-ABL+ AML is associated with high risk of relapse (Neuendorff, 2018). Further, acquired BCR-ABL mutations have been identified as a cause of relapse on FLT3 inhibitors (McMahon, 2019). We hypothesize that BCR-ABL+ AML can be distinguished as a distinct biologic entity from BC-CML by the observation of BCR-ABL as a sub-clonal rather than a founder mutation in leukemogenesis. Methods To test the utility of multi-omic single cell (SC) sequencing in determining tumor phylogeny and identification of BCR-ABL+ AML vs BC-CML, we analyzed bone marrow samples from 4 patients (pts): 1 relapsed AML with clinical development of a new BCR-ABL fusion at the time of relapse, and 3 newly diagnosed BC-CML (2 myeloid, 1 lymphoid). We performed high-throughput SC DNA sequencing with simultaneous cell-surface immunophenotyping and targeted RNA sequencing for BCR-ABL fusion proteins (Mission Bio Tapestri). All SC DNA sequencing included pt-specific primers for BCR-ABL DNA breakpoints, which were previously identified using nanopore-based technology for sequencing long strands of DNA. Results We analyzed 19,197 single cells (median 1995 cells/pt). Multi-omic SC technology differentiated BCR-ABL+ AML from BC-CML by confirming the fusion arose as a branch mutation from a founding leukemic clone and was not present in all leukemic populations. In Patient 1, who was sampled at time of AML relapse, we identified pathogenic FLT3 ITD and NRAS Q61K mutations. FLT3 co-mutated with either the BCR-ABL DNA breakpoint or NRAS in the same single cell and clone, while the BCR-ABL DNA breakpoint and NRAS were not co-mutated. Phylogenetic reconstruction determined FLT3 to be a founding mutation, which later branched into FLT3/BCR-ABL and FLT3/ NRAS co-mutant subclones. Further, the FLT3-ITD/BCR-ABL and FLT3-ITD/ NRAS subclones had distinct immunophenotypes. Blasts harboring the BCR-ABL DNA breakpoint were predominantly identified in the CD117+/CD33+ population (60.2% of cells) compared to CD34+ (16.4% of cells, p <0.001) and blasts with monocytic differentiation (19.1% of cells, p = 0.02). Notably, BCR-ABL+ cells were not identified in T cells. NRAS mutations were predominantly identified in both CD34+ and monocytic blasts. Together, these data support BCR-ABL arising late within the evolutionary history of this pt's AML. By contrast, in Patients 2 and 3 with myeloid BC-CML, the BCR-ABL DNA breakpoint and corresponding BCR-ABL fusion were detectable in all cell populations, including blasts as well as immunophenotypically normal T cells, B cells, and macrophages. In Patient 2, we identified an IDH2 R132C mutation co-mutated with BCR-ABL. Phylogenetic reconstruction determined the IDH2 mutation arose as a branch from a founding BCR-ABL+clone. Unlike the BCR-ABL breakpoint, which was present in all cell populations, the IDH2 mutation was only identified in immunophenotypically-defined CD33+/CD34+/CD117+ myeloid blasts and was absent from bystander T cells and macrophages. Finally, Patient 4had a clinical diagnosis of lymphoid BC-CML. Like Patients 2 and 3, the BCR-ABL DNA breakpoint and corresponding RNA fusion were present in all immunophenotypically-defined blast populations. Unlike Patients 2 and 3, however, BCR-ABL was absent from immunophenotypically normal CD4+ and CD8+ T cells, macrophages, and monocytes. Additional pts are needed to determine whether this pattern is consistent with all lymphoid BC-CML or whether this pt's biology may have been closer to BCR-ABL+ acute lymphoblastic leukemia (ALL). Conclusions Multi-omic SC technology can distinguish BCR-ABL+ AML from myeloid BC-CML and provide direct confirmation that lineage-restriction of BCR-ABL potentially clarifies the diagnosis of discrete Ph+ leukemias. Future studies are needed to understand the role of RT-PCR based BCR-ABL1 testing as a marker of minimal residual disease in pts with BCR-ABL+ subclones, the impact of BCR-ABL as a potential driver of AML resistance, and the utility of BCR-ABL inhibitors in leukemias with late-arising BCR-ABL+ clones. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org. Clinicaltrials.gov Identifiers: NCT00048958, NCT00899223, NCT00900224

A Multiomic, Single-Cell Measurable Residual Disease (scMRD) Assay for Phasing DNA Mutations and Surface Immunophenotypes

The small population of cancerous cells that remain following treatment, known as measurable residual disease (MRD), is the major cause of relapse in acute myeloid leukemia (AML). Usually, these refractory cells have gained additional resistance mutations or changed their surface immunophenotypes in ways that preclude detection and phasing by current gold standard flow cytometry or bulk next-generation sequencing assays. For this reason, a multiomic single-cell MRD (scMRD) assay could offer a more comprehensive indicator of relapse and the potential for faster response. Here, we present a new scMRD assay with a 0.01% limit of detection that provides single-cell clonal architecture and immunophenotyping to not only identify residual leukemia cells, but also identify putative DNA or protein targets for salvage therapy. The assay enables rare-cell detection on a standard Mission Bio Tapestri run by adding (i) an upfront bead-based protocol to enrich for blast cells, (ii) a DNA and protein panel specifically designed for AML MRD diagnosis and treatment, and (iii) a new, automated analysis pipeline to evaluate single-cell multiomics output. By utilizing Mission Bio's single-molecule DNA sensitivity for single cells, this pipeline can identify and correlate co-occurring de novo variants, thereby reducing false positive rates over bulk assays that do not correlate variants. It furthermore can create phylogenetic trees of the detected MRD cells and present their surface protein signature and arm-level copy number. In addition, the multiplexing of up to three patient samples combined in one run via germline identification further reduces per sample costs and increases throughput. To demonstrate these features on 0.01% MRD, samples were constructed by titrating diseased cells into healthy bone marrow cells before processing them with the scMRD assay. We detected 0.01% spike-in (CD34+) and 0.1% spike-in (CD117+) in 6 of 6 samples, with an average enrichment of 41x and 13x, respectively. Further testing detected CD34+ 0.1% spike-ins in 10 of 10 samples (32x average enrichment). We applied the scMRD assay to banked bone marrow aspirate samples from 3 AML patients. The scMRD assay resolved the clonal architecture identifying multiple leukemic clones with co-occurring mutations. The assay readily distinguished pre-leukemic from leukemic clones thereby increasing the specificity of MRD results. The integration of genotype and immunophenotypic further enhanced MRD detection by identifying genotype-specific protein expression patterns. By combining high sensitivity with multiomics, this assay offers a potential scalable solution for comprehensive MRD detection that guides therapeutic decision-making.

Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics.

Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.

Optima: an open-source R package for the Tapestri platform for integrative single cell multiomics data analysis.

The Tapestri platform offers DNA and protein analysis at the single-cell level. Integrating both types of data is beneficial for studying multiple cell populations in heterogeneous microenvironments, such as tumor tissues. Here, we present optima, an R package for the processing and analysis of data generated from the Tapestri platform. This package provides streamlined functionality for raw data filtering, integration, normalization, transformation, and visualization. Insights gained from the optima package help users to identify unique cell populations and uncover surface protein expression patterns. The results generated by optima help researchers elucidate dynamic changes at the single-cell level in heterogeneous microenvironments. This package is available in Github: https://github.com/rachelgriffard/optima.

Multi-omics analysis of mucosal and systemic immunity to SARS-CoV-2 after birth

The dynamics of immunity to infection in infants remain obscure. Here, we used a multi-omics approach to perform a longitudinal analysis of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in infants and young children by analyzing blood samples and weekly nasal swabs collected before, during, and after infection with Omicron and non-Omicron variants. Infection stimulated robust antibody titers that, unlike in adults, showed no sign of decay for up to 300days. Infants mounted a robust mucosal immune response characterized by inflammatory cytokines, interferon (IFN) α, and T helper (Th) 17 and neutrophil markers (interleukin [IL]-17, IL-8, and CXCL1). The immune response in blood was characterized by upregulation of activation markers on innate cells, no inflammatory cytokines, but several chemokines and IFNα. The latter correlated with viral load and expression of interferon-stimulated genes (ISGs) in myeloid cells measured by single-cell multi-omics. Together, these data provide a snapshot of immunity to infection during the initial weeks and months of life.

Flow cytometry analysis of protein expression using antibody-derived tags followed by CITE-Seq.

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) is a single-cell phenotyping method that uses antibody-derived tags (ADTs) to quantitatively detect cell surface protein expression and generate transcriptomic data at the single-cell level. Despite the increased popularity of this technique to study cellular heterogeneity and dynamics, detailed methods on how to choose ADT markers and ensuring reagent performance in biological relevant systems prior to sequencing is not available. Here we describe a novel and easy-to-use multiplex flow proxy assay in which multiple protein markers can be measured simultaneously using a combination of ADT reagents and dye-oligo conjugates by flow cytometry. Using dye-oligo conjugates with sequences complementary to the ADT reagents, we can achieve specific binding and evaluate protein marker expression in a multiplex way. This quality control assay is useful for guiding ADT marker choice and confirming protein expression prior to sequencing. Importantly, the labeled cells can be directly isolated based on the specific fluorescence from dye-oligo conjugates using a flow cytometry cell sorter and processed for downstream single-cell multiomics. Using this streamlined workflow, we sorted natural killer cells and T cells efficiently using only ADT and dye-oligo reagents, avoiding the possibility of decreased marker resolution from co-staining cells with ADT and fluorescent antibodies. This novel workflow provides a viable option for improving ADT marker choice and cell sorting efficiency, allowing subsequent CITE-Seq.

ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis.

Single-cell multiomics techniques have been widely applied to detect the key signature of cells. These methods have achieved a single-molecule resolution and can even reveal spatial localization. These emerging methods provide insights elucidating the features of genomic, epigenomic and transcriptomic heterogeneity in individual cells. However, they have given rise to new computational challenges in data processing. Here, we describe Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal pipeline for barcode-indexed single-cell single-molecule multiomics data analysis. Essentially, the C language is utilized in ScSmOP to set up spaced-seed hash table-based algorithms for barcode identification according to ligation-based barcoding data and synthesis-based barcoding data, followed by data mapping and deconvolution. We demonstrate high reproducibility of data processing between ScSmOP and published pipelines in comprehensive analyses of single-cell omics data (scRNA-seq, scATAC-seq, scARC-seq), single-molecule chromatin interaction data (ChIA-Drop, SPRITE, RD-SPRITE), single-cell single-molecule chromatin interaction data (scSPRITE) and spatial transcriptomic data from various cell types and species. Additionally, ScSmOP shows more rapid performance and is a versatile, efficient, easy-to-use and robust pipeline for single-cell single-molecule multiomics data analysis.

Neurogenesis in primates versus rodents and the value of non-human primate models.

Neurogenesis, the process of generating neurons from neural stem cells, occurs during both embryonic and adult stages, with each stage possessing distinct characteristics. Dysfunction in either stage can disrupt normal neural development, impair cognitive functions, and lead to various neurological disorders. Recent technological advancements in single-cell multiomics and gene-editing have facilitated investigations into primate neurogenesis. Here, we provide a comprehensive overview of neurogenesis across rodents, non-human primates, and humans, covering embryonic development to adulthood and focusing on the conservation and diversity among species. While non-human primates, especially monkeys, serve as valuable models with closer neural resemblance to humans, we highlight the potential impacts and limitations of non-human primate models on both physiological and pathological neurogenesis research.

SARS-CoV-2 infection induces a long-lived pro-inflammatory transcriptional profile

BackgroundThe immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in COVID-19 patients has been extensively investigated. However, much less is known about the long-term effects of infection in patients and how it could affect the immune system and its capacity to respond to future perturbations.MethodsUsing a targeted single-cell multiomics approach, we have recently identified a prolonged anti-inflammatory gene expression signature in T and NK cells in type 1 diabetes patients treated with low-dose IL-2. Here, we investigated the dynamics of this signature in three independent cohorts of COVID-19 patients: (i) the Oxford COVID-19 Multi-omics Blood Atlas (COMBAT) dataset, a cross-sectional cohort including 77 COVID-19 patients and ten healthy donors; (ii) the INCOV dataset, consisting of 525 samples taken from 209 COVID-19 patients during and after infection; and (iii) a longitudinal dataset consisting of 269 whole-blood samples taken from 139 COVID-19 patients followed for a period of up to 7 months after the onset of symptoms using a bulk transcriptomic approach.ResultsWe discovered that SARS-CoV-2 infection leads to a prolonged alteration of the gene expression profile of circulating T, B and NK cells and monocytes. Some of the genes affected were the same as those present in the IL-2-induced anti-inflammatory gene expression signature but were regulated in the opposite direction, implying a pro-inflammatory status. The altered transcriptional profile was detected in COVID-19 patients for at least 2 months after the onset of the disease symptoms but was not observed in response to influenza infection or sepsis. Gene network analysis suggested a central role for the transcriptional factor NF-κB in the regulation of the observed transcriptional alterations.ConclusionsSARS-CoV-2 infection causes a prolonged increase in the pro-inflammatory transcriptional status that could predispose post-acute patients to the development of long-term health consequences, including autoimmune disease, reactivation of other viruses and disruption of the host immune system-microbiome ecosystem.

Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution

Understanding the genetic and nongenetic determinants of tumor protein 53 (TP53)-mutation-driven clonal evolution and subsequent transformation is a crucial step toward the design of rational therapeutic strategies. Here we carry out allelic resolution single-cell multi-omic analysis of hematopoietic stem/progenitor cells (HSPCs) from patients with a myeloproliferative neoplasm who transform to TP53-mutant secondary acute myeloid leukemia (sAML). All patients showed dominant TP53 ‘multihit’ HSPC clones at transformation, with a leukemia stem cell transcriptional signature strongly predictive of adverse outcomes in independent cohorts, across both TP53-mutant and wild-type (WT) AML. Through analysis of serial samples, antecedent TP53-heterozygous clones and in vivo perturbations, we demonstrate a hitherto unrecognized effect of chronic inflammation, which suppressed TP53 WT HSPCs while enhancing the fitness advantage of TP53-mutant cells and promoted genetic evolution. Our findings will facilitate the development of risk-stratification, early detection and treatment strategies for TP53-mutant leukemia, and are of broad relevance to other cancer types.

Abstract 026: Three-dimensional Visualization And Evaluation Of Differences In Renin Expression In Kidneys Exposed In Vivo To Either Low Or High Arterial Pressure

Background: Renin cells are essential for the regulation of blood pressure (BP). In adult mammals, renin cells are present in small numbers in the juxtaglomerular area, but when homeostasis is threatened, such as by hypotension, additional cells along the renal arterioles are recruited to synthesize and release renin. However, there have been no studies to date that have shown changes in the distribution of renin cells in response to changes in BP by three-dimensional (3D) imaging. Objective: Establish a method to visualize and measure renin cells, and comparison of renin expression differences in kidneys exposed to hypo- or hypertension in 3D. Methods: We used our new mouse model ( Ren1 c-tdTomato ) that expresses renin and the fluorescent tdTomato under the control of the endogenous renin gene environment. To test whether we could detect differences in the distribution of renin in response to changes in BP, we subjected Ren1 c-tdTomato mice to aortic coarctation at 3 months of life. The right and left kidneys (RK, LK) are exposed to high and low BPs, respectively. We harvested kidneys after 72 hours and performed tissue-clearing. We used light sheet microscopy for imaging and the Imaris software for 3D reconstruction and data analysis. We defined the 3D renin cell index (3D-RI) as total volume of renin cells (μm 3 ) divided by total number of glomeruli. We extracted 10 cubes of 500 μm square each from the L and R renal cortex, calculated their individual 3D-RI and compared them. Results: With tissue-clearing-based 3D imaging, renin cells were clearly visualized, allowing observation of their distribution and appearance at a one-cell resolution. In the LK exposed to hypotension, several afferent arterioles were identified where renin cells extended close to their branching point. Further, the average value of the 3D-RI was significantly higher in LK (21460 ±6303) than in RK (12838 ± 4632; p = 0.003). Conclusion: Using our new Ren1 c-tdTomato mouse and 3D imaging technology we detected differences in renin cells appearance and distribution in kidneys exposed to hypo- or hypertension. Combined with spatial single cell multiomics, 3D visualization of renin cells will open new avenues for understanding how vascular cells acquire their positional identity in health and disease.

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths.

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1mut erythroid progenitor cells. We uncovered distinct cryptic 3' splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3' splice site usage in SF3B1mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

Divergent CD4+ T-cell profiles are associated with anti-HLA alloimmunization status in platelet-transfused AML patients.

Acute myeloid leukemia (AML) is one of the commonest hematologic disorders. Due to the high frequency of disease- or treatment-related thrombocytopenia, AML requires treatment with multiple platelet transfusions, which can trigger a humoral response directed against platelets. Some, but not all, AML patients develop an anti-HLA immune response after multiple transfusions. We therefore hypothesized that different immune activation profiles might be associated with anti-HLA alloimmunization status. We tested this hypothesis, by analyzing CD4+ T lymphocyte (TL) subsets and their immune control molecules in flow cytometry and single-cell multi-omics. A comparison of immunological status between anti-HLA alloimmunized and non-alloimmunized AML patients identified differences in the phenotype and function of CD4+ TLs. CD4+ TLs from alloimmunized patients displayed features of immune activation, with higher levels of CD40 and OX40 than the cells of healthy donors. However, the most notable differences were observed in non-alloimmunized patients. These patients had lower levels of CD40 and OX40 than alloimmunized patients and higher levels of PD1. Moreover, the Treg compartment of non-alloimmunized patients was larger and more functional than that in alloimmunized patients. These results were supported by a multi-omics analysis of immune response molecules in conventional CD4+ TLs, Tfh circulating cells, and Tregs. Our results thus reveal divergent CD4+ TL characteristics correlated with anti-HLA alloimmunization status in transfused AML patients. These differences, characterizing CD4+ TLs independently of any specific antigen, should be taken into account when considering the immune responses of patients to infections, vaccinations, or transplantations.

SARS-CoV-2 Omicron BA.1 breakthrough infection drives late remodeling of the memory B cell repertoire in vaccinated individuals

How infection by a viral variant showing antigenic drift impacts a preformed mature human memory B cell (MBC) repertoire remains an open question. Here, we studied the MBC response up to 6months after SARS-CoV-2 Omicron BA.1 breakthrough infection in individuals previously vaccinated with three doses of the COVID-19 mRNA vaccine. Longitudinal analysis, using single-cell multi-omics and functional analysis of monoclonal antibodies from RBD-specific MBCs, revealed that a BA.1 breakthrough infection mostly recruited pre-existing cross-reactive MBCs with limited de novo response against BA.1-restricted epitopes. Reorganization of clonal hierarchy and new rounds of germinal center reactions, however, combined to maintain diversity and induce progressive maturation of the MBC repertoire against common Hu-1 and BA.1, but not BA.5-restricted, SARS-CoV-2 Spike RBD epitopes. Such remodeling was further associated with a marked improvement in overall neutralizing breadth and potency. These findings have fundamental implications for the design of future vaccination booster strategies.

Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics.

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.