Single-cell Multiomic Analysis Research Articles

Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine the effects of brain disorders and treatments. Despite the recent surge in the amount of available neuron morphology reconstructions due to advancements in microscopy imaging, existing computational and deep learning methods for modeling neuron morphology have been limited in both scale and accuracy. In this paper, we propose MorphRep, a model for learning meaningful representation of neuron morphology pre-trained with over 250 000 existing neuron morphology data. By encoding the neuron morphology into graph-structured data, using graph transformers for feature encoding and enforcing the consistency between multiple augmented views of neuron morphology, MorphRep achieves the state of the art performance on widely used benchmarking datasets. Meanwhile, MorphRep can accurately characterize the neuron morphology space across neuron morphometrics, fine-grained cell types, brain regions and ages. Furthermore, MorphRep can be applied to distinguish neurons under a wide range of conditions, including genetic perturbation, drug injection, environment change and disease. In summary, MorphRep provides an effective strategy to embed and represent neuron morphology and can be a valuable tool in integrating cell morphology into single-cell multiomics analysis. The codebase has been deposited in https://github.com/YaxuanLi-cn/MorphRep.

New pathways to neurogenesis: Insights from injury-induced retinal regeneration.

The vertebrate retina is a tractable system for studying control of cell neurogenesis and cell fate specification. During embryonic development, retinal neurogenesis is under strict temporal regulation, with cell types generated in fixed but overlapping temporal intervals. The temporal sequence and relative numbers of retinal cell types generated during development are robust and show minimal experience-dependent variation. In many cold-blooded vertebrates, acute retinal injury induces a different form of neurogenesis, where Müller glia reprogram into retinal progenitor-like cells that selectively regenerate retinal neurons lost to injury. The extent to which the molecular mechanisms controlling developmental and injury-induced neurogenesis resemble one another has long been unclear. However, a recent study in zebrafish has shed new light on this question, using single-cell multiomic analysis to show that selective loss of different retinal cell types induces the formation of fate-restricted Müller glia-derived progenitors that differ both from one another and from progenitors in developing retina. Here, we discuss the broader implications of these findings, and their possible therapeutic relevance.

IBCL-541 A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

IBCL-541 A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

A Comprehensive Single-Cell Multi-Omic Analysis to Depict the Biological Heterogeneity of Follicular and Large B-Cell Lymphomas

Single-cell multiomics analysis reveals cell/tissue-specific associations in bipolar disorder

This study investigates the cellular origin and tissue heterogeneity in bipolar disorder (BD) by integrating multiomics data. Four distinct datasets were employed, including single-cell RNA sequencing (scRNA-seq) data (embryonic and fetal brain, n = 8, 1,266 cells), BD Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (adult brain, n = 210), BD bulk RNA-seq data (adult brain, n = 314), and BD genome-wide association study (GWAS) summary data (n = 413,466). The integration of scRNA-seq data with multiomics data relevant to BD was accomplished using the single-cell disease relevance score (scDRS) algorithm. We have identified a novel brain cell cluster named ADCY1, which exhibits distinct genetic characteristics. From a high-resolution genetic perspective, glial cells emerge as the primary cytopathology associated with BD. Specifically, astrocytes were significantly related to BD at the RNA-seq level, while microglia showed a strong association with BD across multiple panels, including the transcriptome-wide association study (TWAS), ATAC-seq, and RNA-seq. Additionally, oligodendrocyte precursor cells displayed a significant association with BD in both ATAC-seq and RNA-seq panel. Notably, our investigation of brain regions affected by BD revealed significant associations between BD and all three types of glial cells in the dorsolateral prefrontal cortex (DLPFC). Through comprehensive analyses, we identified several BD-associated genes, including CRMP1, SYT4, UCHL1, and ZBTB18. In conclusion, our findings suggest that glial cells, particularly in specific brain regions such as the DLPFC, may play a significant role in the pathogenesis of BD. The integration of multiomics data has provided valuable insights into the etiology of BD, shedding light on potential mechanisms underlying this complex psychiatric disorder.

Decoding molecular features of bovine oocyte fate during antral follicle growth via single-cell multi-omics analysis.

Antral follicle size is a useful predictive marker of the competency of enclosed oocytes for yielding an embryo following in vitro maturation and fertilization. However, the molecular mechanisms underpinning oocyte developmental potential during bovine antral follicle growth are still unclear. Here, we used a modified single-cell multi-omics approach to analyze the transcriptome, DNA methylome and chromatin accessibility in parallel for oocytes and cumulus cells collected from bovine antral follicles of different sizes. Transcriptome profiling identified three types of oocytes (Small, Medium and Large) that underwent different developmental trajectories, with Large oocytes exhibiting the largest average follicle size and characteristics resembling metaphase-II oocytes. Differential expression analysis and real-time PCR assay showed that most replication-dependent histone genes were highly expressed in Large oocytes. The joint analysis of multi-omics data revealed that the transcription of 20 differentially expressed genes in Large oocytes was associated with both DNA methylation and chromatin accessibility. In addition, oocyte-cumulus interaction analysis showed that inflammation, DNA damage, and p53 signaling pathways were active in Small oocytes, which had the smallest average follicle sizes. We further confirmed that p53 pathway inhibition in in vitro maturation experiments using oocytes obtained from small antral follicles could improve the quality of oocytes and increased the blastocyte rate after in vitro fertilization and culture. Our work provides new insights into the intricate orchestration of bovine oocyte fate determination during antral folliculogenesis, which is instrumental for optimizing in vitro maturation techniques to optimize oocyte quality.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

Single-cell multi-omics analysis reveals cooperative transcription factors for gene regulation in oligodendrocytes.

Oligodendrocytes are the myelinating cells within the central nervous system. Many oligodendrocyte genes have been associated with brain disorders. However, how transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remains largely uncharacterized. To address this, we integrated scRNA-seq and scATAC-seq data to identify the cooperative TFs that co-regulate the target gene (TG) expression in oligodendrocytes. First, we identified co- binding TF pairs whose binding sites overlapped in oligodendrocyte-specific regulatory regions. Second, we trained a deep learning model to predict the expression level of each TG using the expression levels of co-binding TFs. Third, using the trained models, we computed the TF importance and TF-TF interaction scores for predicting TG expression by the Shapley interaction scores. We found that the co-binding TF pairs involving known important TF pairs for oligodendrocyte differentiation, such as SOX10-TCF12, SOX10-MYRF, and SOX10-OLIG2, exhibited significantly higher Shapley scores than others (t-test, p-value < 1e-4). Furthermore, we identified 153 oligodendrocyte-associated eQTLs that reside in oligodendrocyte-specific enhancers or promoters where their eGenes (TGs) are regulated by cooperative TFs, suggesting potential novel regulatory roles from genetic variants. We also experimentally validated some identified TF pairs such as SOX10-OLIG2 and SOX10-NKX2.2 by co-enrichment analysis, using ChIP-seq data from rat peripheral nerve.

105-OR: Single-Cell Multiomic Analysis of the Effect of GIP and GIP-1 Mono- and Multi-agonism on the Hypothalamus and Hindbrain

105-OR: Single-Cell Multiomic Analysis of the Effect of GIP and GIP-1 Mono- and Multi-agonism on the Hypothalamus and Hindbrain

A single-cell multiomics analysis of IRF4 in mediating treatment resistance in ibrutinib-treated chronic lymphocytic leukemia (CLL).

7051 Background: The Bruton tyrosine kinase (BTK) inhibitor ibrutinib (ibr) has revolutionized the treatment of CLL, showing efficacy in the majority of patients. Nevertheless, resistance occurs in a subset of patients, often with dismal clinical outcomes. Ibr resistance occurs through several mechanisms, including mutations affecting BTK or PLCG2, or upregulation of alternative survival pathways. We investigated at the single-cell level the molecular mechanisms that underlie ibr resistance in CLL cells and their interactions with non-malignant tumor microenvironment (TME) cell populations. Methods: We retrospectively identified 7 CLL patients with ibr resistance (as second line therapy). Samples were obtained before ibr treatment and at progression. Chromium Single Cell Multiome ATAC + Gene Expression (GEX) (10x Genomics) was applied to a mixture of CD19+CD5+ and CD19- cells to jointly analyze DNA accessibility and gene expression in the same single nuclei of CLL B and TME cells. An average of 4,800 cells (range 2,600-15,000) per sample was retained after QC filtering. Principal component analysis (PCA) was performed on GEX counts. ATAC peaks were called with MACS2, and latent semantic indexing was used for dimensionality reduction. We then computed a weighted nearest neighbour graph, identified clusters using the SLM algorithm and performed cell type assignment with established marker genes. Results: We identified 28 distinct cell clusters, representing CLL B-cells or cell types that constitute the TME, including CD4+ and CD8+ T cells, monocytes, dendritic and NK cells. CLL B-cell clusters were patient and timepoint-specific, while TME clusters were occupied by cells from multiple patients. Five patients showed multiple distinct CLL B-cell clusters pre-treatment or at progression, reflecting cellular heterogeneity within the tumor compartment. Interestingly, some clusters that were dominated by post-treatment CLL B-cells also included cells from pre-treatment samples, suggesting pre-existence of the clone giving rise to the relapse. For each patient, we performed pairwise differential expression between CLL B-cell clusters. Gene ontology analysis revealed increased activation of IRF4 and MHC-related pathways in CLL B-cells at progression compared to pre-treatment in 6 and 4 out of 7 patients, respectively. The elevated IRF4 pathway activity was underpinned by increased chromatin accessibility in IRF4 signature genes. Conclusions: Differential blockade of B-cell receptor signaling by ibr has been reported to downregulate activity of IRF4, a key transcription factor in B-cell activation. Our results suggest that CLL B-cells that are resistant to ibrutinib have re-activated IRF4 activity beyond pre-treatment levels, implying a potential role for IRF4 in ibrutinib resistance.

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation.

Despite the success of antiretroviral therapy, human immunodeficiency virus (HIV) cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) approach to simultaneously profile the transcriptomic and epigenomic characteristics of ∼ 125,000 latently infected primary CD4+ T cells after reactivation using three different latency reversing agents. Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79% accurate at predicting viral reactivation. Finally, we validated the role of two candidate HIV-regulating factors, FOXP1 and GATA3, in viral transcription. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

Immune cell senescence and exhaustion promote the occurrence of liver metastasis in colorectal cancer by regulating epithelial-mesenchymal transition.

Liver metastasis (LM) stands as a primary cause of mortality in metastatic colorectal cancer (mCRC), posing a significant impediment to long-term survival benefits from targeted therapy and immunotherapy. However, there is currently a lack of comprehensive investigation into how senescent and exhausted immune cells contribute to LM. We gathered single-cell sequencing data from primary colorectal cancer (pCRC) and their corresponding matched LM tissues from 16 mCRC patients. In this study, we identified senescent and exhausted immune cells, performed enrichment analysis, cell communication, cell trajectory, and cell-based in vitro experiments to validate the results of single-cell multi-omics. This process allowed us to construct a regulatory network explaining the occurrence of LM. Finally, we utilized weighted gene co-expression network analysis (WGCNA) and 12 machine learning algorithms to create prognostic risk model. We identified senescent-like myeloid cells (SMCs) and exhausted T cells (TEXs) as the primary senescent and exhausted immune cells. Our findings indicate that SMCs and TEXs can potentially activate transcription factors downstream via ANGPTL4-SDC1/SDC4, this activation plays a role in regulating the epithelial-mesenchymal transition (EMT) program and facilitates the development of LM, the results of cell-based in vitro experiments have provided confirmation of this conclusion. We also developed and validated a prognostic risk model composed of 12 machine learning algorithms. This study elucidates the potential molecular mechanisms underlying the occurrence of LM from various angles through single-cell multi-omics analysis in CRC. It also constructs a network illustrating the role of senescent or exhausted immune cells in regulating EMT.

OA27 Molecular divergences in CD4+ T Cells between early and refractory rheumatoid arthritis

Abstract Background/Aims Up to 21% of rheumatoid arthritis (RA) patients remain refractory to available treatments. This represents a clinical challenge due to the lack of efficacious alternatives for refractory RA (rRA) patients. Although CD4+ T cells are paramount in RA pathogenesis, a comprehensive understanding of their roles in rRA versus non-refractory RA has not been characterised. The aim of this study is to dissect the molecular differences in CD4+ T cells across these patient sub-groups and identify novel mechanisms driving rRA, which may inform tailored therapeutic strategies, using single cell multiomic analysis. Methods Primary CD4+ T-cells were isolated from PBMCs obtained from 3 rRA patients (defined as per EULAR definition of difficult-to-treat RA) and 3 treatment-naïve, early RA (ERA) patients, all seropositive with active disease (DAS28-ESR &amp;gt; 3.2), collected as part of the PRECISE-RA programme. Following nuclei extraction, the 10x Genomics Multiome ATAC + Gene Expression assay was used to simultaneously determine the transcriptional and chromatin landscapes from individual cells. Results Patient characteristics are reported in Table 1. 8,215 T-cells cells were clustered into 11 distinct cell states, which showed a trend towards diminished Tregs and modulated effector memory (TEM)/Th17 phenotype expressions in rRA compared to ERA patients. In ERA, expression of INPP4B, pivotal for cell growth and survival, was upregulated within naïve CD4+ T-cell subsets. ITGA4, crucial in leukocyte migration and adhesion, was elevated within a subset of naïve CD4+ T cells in early RA. ZEB2 exhibited increased expression within CD4 TEM/Th17 phenotype cells, potentially implicating divergent cellular differentiation, and migratory pathways within early RA contexts. Next, we utilised the multiomic nature of the data to functionally annotate GWAS variants, by creating peak-gene maps and overlaying RA risk SNPs. For example, an RA susceptibility SNP mapping to an intron of ANKRD55, rs7731626, was linked to the novel RA causal gene IL6ST. Furthermore, the RA SNP rs71508903, previously assigned to the ARID5B gene, was linked to the RTKN2 promoter, elucidating potential avenues for further exploration into RA pathogenesis. Conclusion These findings, to be validated in a larger cohort, suggest distinct biological mechanisms driving rRA and may help identify novel biomarkers and therapeutic avenues. Disclosure S.L. Smith: None. S. Rossi: None. R. Shukla: None. Y. Tan: None. A. Ariff: None. I. Lazar: None. K. Patel: None. C.F. Yap: None. N. Nair: None. S. Eyre: None. M.H. Buch: None. M.H. Buch: None. P. Martin: None. G. Orozco: None.

Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

Chronic hypoxia remodels the tumor microenvironment to support glioma stem cell growth

Cerebral organoids co-cultured with patient derived glioma stem cells (GLICOs) are an experimentally tractable research tool useful for investigating the role of the human brain tumor microenvironment in glioblastoma. Here we describe long-term GLICOs, a novel model in which COs are grown from embryonic stem cell cultures containing low levels of GSCs and tumor development is monitored over extended durations (ltGLICOs). Single-cell profiling of ltGLICOs revealed an unexpectedly long latency period prior to GSC expansion, and that normal organoid development was unimpaired by the presence of low numbers of GSCs. However, as organoids age they experience chronic hypoxia and oxidative stress which remodels the tumor microenvironment to promote GSC expansion. Receptor-ligand modelling identified astrocytes, which secreted various pro-tumorigenic ligands including FGF1, as the primary cell type for GSC crosstalk and single-cell multi-omic analysis revealed these astrocytes were under the control of ischemic regulatory networks. Functional validation confirmed hypoxia as a driver of pro-tumorigenic astrocytic ligand secretion and that GSC expansion was accelerated by pharmacological induction of oxidative stress. When controlled for genotype, the close association between glioma aggressiveness and patient age has very few proposed biological explanations. Our findings indicate that age-associated increases in cerebral vascular insufficiency and associated regional chronic cerebral hypoxia may contribute to this phenomenon.

Abstract 1690: LSD1 and CoREST2 demethylate STAT3 to promote enteroendocrine cell differentiation in mucinous colorectal cancer

Abstract Across different cancer types, tumor heterogeneity has been shown to drive tumor progression, metastasis, and therapeutic resistance. Adenocarcinoma to neuroendocrine lineage transition is an emergent mechanism of targeted therapy resistance in several cancer types, including lung and prostate cancer. Therefore, understanding the dynamics and mechanisms driving neuroendocrine cell fates in cancer is critical. Mucinous colorectal cancer accounts for upwards of 20% of colorectal cancer cases and is characterized by tumors with mucous accounting for at least 50% of the tumor volume. The normal colon epithelium consists of several specialized cell types, including hormone secreting enteroendocrine cells (EECs), which are the neuroendocrine cell of the intestine. We have previously shown that EEC progenitors are enriched in mucinous colorectal cancer and promote cancer cell survival via secreted factors. Additionally, we have shown that lysine specific demethylase 1 (LSD1) promotes EEC differentiation in these tumors; however, the mechanism by which LSD1 promotes EEC differentiation has remained unknown. Typically to carry out its enzymatic function, LSD1 must be a part of a transcriptional regulatory complex. One such complex is the CoREST complex, which contains LSD1, HDAC1, and one of three CoREST protein family members. Here we report that LSD1 and CoREST2 promote EEC differentiation by demethylating the transcription factor signal transducer and activator of transcription 3 (STAT3) to promote STAT3 chromatin binding. Additionally, we demonstrate that knocking down CoREST2 decreases tumor growth and lung metastases of mucinous colorectal cancer cells injected orthotopically into the colons of immunocompromised mice. Furthermore, we utilized single cell multi-omics that combines single cell RNA sequencing with single cell ATAC sequencing to show that during EEC differentiation there is an increase in chromatin accessibility at regulatory regions of known EEC-promoting transcription factors. Finally, through our single cell multi-omics analysis we identify a new rare cell type with neuron-like but not secretory characteristics. Mucinous colorectal cancer is an aggressive and chemotherapeutically intractable form of CRC that is enriched for EEC progenitors. Our data demonstrates that LSD1 and STAT3 promote EEC differentiation, suggesting that inhibiting LSD1 and or STAT3 may have benefits for patients with mucinous colorectal cancer. Citation Format: Christopher A. Ladaika, Ahmed H. Ghobashi, William C. Boulton, Heather M. O'Hagan. LSD1 and CoREST2 demethylate STAT3 to promote enteroendocrine cell differentiation in mucinous colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1690.

Abstract 3501: Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis

Abstract Chordoma, a rare bone cancer characterized by limited actionable clinical targets, exhibits a pivotal hallmark in the activation of the developmental transcription factor gene TBXT (Brachyury) through its association with highly active enhancers. TBXT amplification emerges as a major causative factor in familial chordomas, yet the amplification mechanism and its consequential impact on genomic instability remain inadequately elucidated. In this study, we conducted a comprehensive analysis of multiple genomic and epigenomic sequencing datasets, including WGS-seq, H3K27ac ChIP-seq, and ATAC-seq in six chordoma cell lines, and HiC data in two of them, to unravel the mechanisms of structural variations and their biological implications. Notably, our focus extends to JHC7 cells, which exhibit extrachromosomal amplification of a 2.4M region encompassing TBXT and the associated enhancers. The existence of such extrachromosomal DNA (ecDNA) in JHC7 was further validated through DNA FISH and Sanger sequencing. Comparing JHC7 with CH22, a chordoma cell line lacking TBXT amplification, revealed heightened chromatin relaxation and increased DNA rearrangements in JHC7. Interestingly, FISH results showed significant genomic ploidy heterogeneity within JHC7 cells, a finding demonstrated by nucleus FACS sorting. Single-cell multiomic analysis, combining snRNA-seq and snATAC-seq, demonstrated heterogeneous gene expression and distinct copy number variations among JHC7 cells. However, the amplification of the TBXT locus was observed in nearly all cells examined, indicating the rapid genomic evolution in the cultured JHC7 cells. These results collectively show that TBXT is extensively amplified through the formation of ecDNA, providing a fitness advantage to chordoma cells and concurrently exacerbating genomic instability. The findings in this study provide an insight for understanding the pathogenesis of chordoma and may inform the development of targeted therapeutic strategies. Citation Format: Bo Zhao, Martin Barrio Ines, Lingqun Ye, Xizi Wu, Alice Soragni, Andrew Futreal, Kadir Akdemir. Concurrent amplification of TBXT and highly activated enhancers in extrachromosomal DNA (ecDNA) drives chordoma tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3501.

Abstract 882: Harnessing single cell multi-omics data to identify predictors of clinical outcome in CD19 CAR T cell therapy

Abstract Background: Chimeric antigen receptor (CAR) T cell therapy is effective in treating B-cell malignancies, but factors influencing the persistence of functional CAR+ T cells, such as product composition, patients’ lymphodepletion, and immune reconstitution, are not well understood. To shed light on this issue, we conducted single-cell multi-omics analysis of transcriptional, clonal, and phenotypic profiles from pre- to 1-month post-infusion of CAR+ and CAR− T cells from patients who received a donor-derived 4-1BB CAR product targeting CD19. Methods: Longitudinal blood samples and infusion products were obtained from 10 individuals with large B cell lymphoma or B-cell acute lymphoblastic leukemia (CARTELL cohort in Sydney Australia1). CD45+ cells and enriched CAR+ T cells were single cell sorted for simultaneous scRNA-seq, 41 barcoded proteins (N=41) and T cell receptors, n= 115,000 cells across 21 samples. Machine learning methods were used to identify subsets of immune cells associated with clinically relevant parameters. Results: Following infusion, CAR+ and CAR− T cells showed similar differentiation profiles with clonally expanded populations across heterogeneous phenotypes, demonstrating clonal lineages and phenotypic plasticity. These findings were validated by performing the same analysis on two publicly available scRNA-seq data sets from two cohorts 2, 3 of paediatric (N=18) and adult (N=32) individual with large B cell lymphoma who received CAR19 T cells. Analysis of mass-cytometry data from our cohort and from 28 patients with B cell lymphoma treated with commercial CAR19 T cells (axi-cel)4, identified an association of CAR− and also CAR+ T cells, both expressing an 2B4− CD11b+ CD8+ phenotype, with complete remission or progressive disease at 3 and 6 months post-infusion. Finally, we report additional analysis on the immunogenomics of circulating CAR T cells in two patients who developed CAR T cell induced lymphoma and compared it with ascites and CAR-Lymphoma cells. Discussion: These results suggest that CAR+ and CAR− CD8+ T cells share a differentiation trajectory terminating in an NK-like phenotype that is associated with clinical outcome, and that non-CAR-derived signals may influence patients’ immune recovery and outcomes.

Dynamic immune recovery process after liver transplantation revealed by single-cell multi-omics analysis

Elucidating the temporal process of immune remodeling under immunosuppressive treatment after liver transplantation (LT) is critical for precise clinical management strategies. Here, we performed a single-cell multi-omics analysis of peripheral blood mononuclear cells (PBMCs) collected from LT patients (with and without acute cellular rejection [ACR]) at 13 time points. Validation was performed in two independent cohorts with additional LT patients and healthy controls. Our study revealed a four-phase recovery process after LT and delineated changes in immune cell composition, expression programs, and interactions along this process. The intensity of the immune response differs between the ACR and non-ACR patients. Notably, the newly identified inflamed NK cells, CD14+RNASE2+ monocytes, and FOS-expressing monocytes emerged as predictive indicators of ACR. This study illuminates the longitudinal evolution of the immune cell landscape under tacrolimus-based immunosuppressive treatment during LT recovery, providing a four-phase framework that aids the clinical management of LT patients.