Single-cell Gene Expression Profiling Research Articles

Physiological and pathogenic T cell autoreactivity converge in type 1 diabetes

Autoimmune diseases result from autoantigen-mediated activation of adaptive immunity; intriguingly, autoantigen-specific T cells are also present in healthy donors. An assessment of dynamic changes of this autoreactive repertoire in both health and disease is thus warranted. Here we investigate the physiological versus pathogenic autoreactive processes in the context of Type 1 diabetes (T1D) and one of its landmark autoantigens, glutamic acid decarboxylase 65 (GAD65). Using single cell gene expression profiling and tandem T cell receptor (TCR) sequencing, we find that GAD65-specific true naïve cells are present in both health and disease, with GAD65-specific effector and memory responses showing similar ratios in healthy donors and patients. Deeper assessment of phenotype and TCR repertoire uncover differential features in GAD65-specific TCRs, including lower clonal sizes of healthy donor-derived clonotypes in patients. We thus propose a model whereby physiological autoimmunity against GAD65 is needed during early life, and that alterations of these physiological autoimmune processes in predisposed individuals trigger overt Type 1 diabetes.

Targeting immune-fibroblast cell communication in heart failure.

Inflammation and tissue fibrosis co-exist and are causally linked to organ dysfunction1,2. However, the molecular mechanisms driving immune-fibroblast cell communication in human cardiac disease remain unexplored and there are at present no approved treatments that directly target cardiac fibrosis3,4. Here we performed multiomic single-cell gene expression, epitope mapping and chromatin accessibility profiling in 45 healthy donor, acutely infarcted and chronically failing human hearts. We identified a disease-associated fibroblast trajectory that diverged into distinct populations reminiscent of myofibroblasts and matrifibrocytes, the latter expressing fibroblast activator protein (FAP) and periostin (POSTN). Genetic lineage tracing of FAP+ fibroblasts in vivo showed that they contribute to the POSTN lineage but not the myofibroblast lineage. We assessed the applicability of experimental systems to model cardiac fibroblasts and demonstrated that three different in vivo mouse models of cardiac injury were superior compared with cultured human heart and dermal fibroblasts in recapitulating the human disease phenotype. Ligand-receptor analysis and spatial transcriptomics predicted that interactions between C-C chemokine receptor type 2(CCR2) macrophages and fibroblasts mediated by interleukin-1β (IL-1β) signalling drove the emergence of FAP/POSTN fibroblasts within spatially defined niches. In vivo, we deleted the IL-1 receptor on fibroblasts and the IL-1β ligand in CCR2+ monocytes and macrophages, and inhibited IL-1β signalling using a monoclonal antibody, and showed reduced FAP/POSTN fibroblasts, diminished myocardial fibrosis and improved cardiac function. These findings highlight the broader therapeutic potential of targeting inflammation to treat tissue fibrosis and preserve organ function.

Identification and Targeting of Regulators of SARS-CoV-2-Host interactions in the Airway Epithelium.

Although the impact of SARS-CoV-2 in the lung has been extensively studied, the molecular regulators and targets of the host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. This is in part ascribed to the use of non-primary cell systems, overreliance on single-cell gene expression profiling that not ultimately reflect protein activity and bias toward the downstream effects rather than their mechanistic determinants. Here we address these issues by network-based analysis of single cell transcriptomic profiles of pathophysiologically relevant human adult basal, ciliated and secretory cells to identify master regulator (MR) protein modules controlling their SARS-CoV-2-mediated reprogramming. This uncovered chromatin remodeling, endosomal sorting, ubiquitin pathway as well as proviral factors identified by CRISPR analyses as components of the host response collectively or selectively activated in these cells. Large-scale perturbation assays, using a clinically-relevant drug library, identified 11 drugs able to invert the entire MR signature activated by SARS-CoV-2 in these cell types. Leveraging MR analysis and perturbational profiles of human primary cells, represents a novel mechanism-based approach and resource that can be directly generalized to interrogate signatures of other airway conditions for drug prioritization.

Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencing data and machine learning to classify AML patients and healthy, monocytes, dendritic and progenitor cells population. We found that gene expression profiles of AML patients and healthy controls can be classified at the individual level with high accuracy (>70 %) when using progenitor cells, suggesting the existence of subject-specific single cell transcriptomics profiles. The analysis also revealed molecular determinants of patient heterogeneity (e.g. TPSD1, CT45A1, and GABRA4) which could support new strategies for patient stratification and personalized treatment in leukaemia.

Reduction of myeloid-derived suppressor cells in prostate cancer murine models and patients following white button mushroom treatment.

In a previously reported Phase I trial, we observed therapy-associated declines in circulating myeloid-derived suppressor cells (MDSCs) with the administration of white button mushroom (WBM) tablets in prostate cancer (PCa) patients. These observations led us to hypothesise that WBM could mitigate PCa progression by suppressing MDSCs. We performed bidirectional translational research to examine the immunomodulatory effects of WBM consumption in both syngeneic murine PCa models and patients with PCa participating in an ongoing randomised Phase II trial (NCT04519879). In murine models, WBM treatment significantly suppressed tumour growth with a reduction in both the number and function of MDSCs, which in turn promoted antitumour immune responses mediated by T cells and natural killer (NK) cells. In patients, after consumption of WBM tablets for 3 months, we observed a decline in circulating polymorphonuclear MDSCs (PMN-MDSCs), along with an increase in cytotoxic CD8+ T and NK cells. Furthermore, single immune cell profiling of peripheral blood from WBM-treated patients showed suppressed STAT3/IRF1 and TGFβ signalling in circulating PMN-MDSCs. Subclusters of PMN-MDSCs presented transcriptional profiles associated with responsiveness to fungi, neutrophil chemotaxis, leukocyte aggregation, and regulation of inflammatory response. Finally, in mouse models of PCa, we found that WBM consumption enhanced the anticancer activity of anti-PD-1 antibodies, indicating that WBM may be used as an adjuvant therapy with immune checkpoint inhibitors. Our results from PCa murine models and patients provide mechanistic insights into the immunomodulatory effects of WBM and provide a scientific foundation for WBM as a nutraceutical intervention to delay or prevent PCa progression. White button mushroom (WBM) treatment resulted in a reduction in pro-tumoural MDSCs, notably polymorphonuclear MDSCs (PMN-MDSCs), along with activation of anti-tumoural T and NK cells. Human single immune cell gene expression profiling shed light on the molecular alterations induced by WBM, specifically on PMN-MDSCs. A proof-of-concept study combining WBM with PD-1 blockade in murine models revealed an additive effect on tumour regression and survival outcomes, highlighting the clinical relevance of WBM in cancer management.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Cross-species imputation and comparison of single-cell transcriptomic profiles.

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.

We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.

Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors

Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and Tcell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory Tcells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive Tcells, particularly terminally exhausted CD8+ Tcells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

Simultaneous single-cell three-dimensional genome and gene expression profiling uncovers dynamic enhancer connectivity underlying olfactory receptor choice

The simultaneous measurement of three-dimensional (3D) genome structure and gene expression of individual cells is critical for understanding a genome’s structure–function relationship, yet this is challenging for existing methods. Here we present ‘Linking mRNA to Chromatin Architecture (LiMCA)’, which jointly profiles the 3D genome and transcriptome with exceptional sensitivity and from low-input materials. Combining LiMCA and our high-resolution scATAC-seq assay, METATAC, we successfully characterized chromatin accessibility, as well as paired 3D genome structures and gene expression information, of individual developing olfactory sensory neurons. We expanded the repertoire of known olfactory receptor (OR) enhancers and discovered unexpected rules of their dynamics: OR genes and their enhancers are most accessible during early differentiation. Furthermore, we revealed the dynamic spatial relationship between ORs and enhancers behind stepwise OR expression. These findings offer valuable insights into how 3D connectivity of ORs and enhancers dynamically orchestrate the ‘one neuron–one receptor’ selection process.

Abstract 2278: Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling

Abstract Non-small cell lung cancer (NSCLC) is a type of epithelial lung cancer other than small cell lung cancer and represents the majority of cases in lung cancer. The immune response against NSCLC relies significantly on the vital role by CD4+ T cells, contributing to the regulation of immune system and the suppression of tumor growth. Various CD4+ T cell subtypes, such as T conventional cells (Tconv cells) and T regulatory cells (Treg cells), perform distinct functions within the immune system. Tconv cells boost immune responses against specific pathogens, while Treg cells play a role in suppressing immune reactions. Investigating the correlation between Tconv cells and Treg cells in NSCLC could serve a valuable biomarker for evaluating the immune status of lung cancer patients. We examine the connection between particular CD4+ T cells in the lung and specific gene expressions associated with NSCLC. Through single-cell analysis, we classified the proportions of Treg cells and Tconv cells within CD4+ T cells in both the lungs and spleen of mice. Our discovery revealed that the proportion of Tconv cells in the lungs exceeded that in the spleen. From the differential gene expression (DEG) analysis using RNA-Seq data for Treg cells and Tconv cells in NSCLC, compared to the control group of Peripheral Blood Mononuclear Cells (PMBC), we identified 1823 genes uniquely expressed in NSCLC Treg cells and 711 genes in Tconv cells. Within these genes, we verified the expression of IL2RA and CTLA4, known to be expressed in NSCLC. Additionally, we identified high expression of genes TM4SF1 and KRT6A. By comparing the genes specific to Tconv and Treg cells in the lungs with those significantly expressed in NSCLC, we were able to identify 114 co-expressed genes in Treg cells and 71 in Tconv cells. We conducted GO and KEGG pathway analyses on these genes and examined the interaction networks among the associated genes. Furthermore, we found receptor interactions between Tconv cell subtypes (Th1, Th2, Th17, and Tfh) and Treg cells through cell-cell interaction analysis. We explored the mouse phenotypes associated with these genes. These discoveries unveiled connections within lung-specific gene networks of Treg and Tconv cells associated with NSCLC, highlighting candidate genes for targeted therapy in cancer. Citation Format: Hyunmin Woo, JiHye Yang, Siyoung Yang, Seong-il Eyun. Unraveling immune dynamics of CD4+ in non-small cell lung cancer through single-cell analysis and gene expression profiling [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 2278.

Abstract 5352: SOX9-dependent immune microenvironment remodeling drives KRAS-induced lung adenocarcinoma progression

Abstract Interactions between tumor intrinsic factors and tumor microenvironment play a vital role in disease progression and treatment response. SOX9, a transcription factor crucial for tissue development and homeostasis, has been linked to the advancement of tumor growth. However, its specific role as a driver in lung adenocarcinoma (LUAD) is not well-defined. Additionally, the impact of SOX9 on the tumor microenvironment has not been previously explored. We here identify SOX9 transcription factor to play a critical role in defining tumor immune microenvironment and driving disease progression. We employed CRISPR/Cas9 and Cre-LoxP gene knockout methods within the Kras G12D-induced mouse model of lung adenocarcinoma (LUAD). Our objective was to investigate the mechanisms through which SOX9 contributes to the development and advancement of lung adenocarcinoma. To substantiate our findings, we conducted immune profiling, gene expression analyses, RT-qPCR, and immunohistochemistry assessments in the Kras G12D-driven murine LUAD. These results were subsequently validated by examining bulk and single-cell gene expression profiles, as well as immunohistochemistry, in human lung adenocarcinoma. Our studies indicated that SOX9 played a very important role by enhancing lung tumor development, burden, and progression, leading to lower overall survival. SOX9. SOX9 consistently facilitated the growth of organoids in vitro. However, the promotion of tumor growth by SOX9 was notably reduced in immunocompromised mice compared to syngeneic mice. SOX9 demonstrated a suppressive effect on immune cell infiltration, particularly affecting the functionality of tumor-associated CD8+ T cells, natural killer cells, and dendritic cells. CD8+ T cells in the SOX9 overexpressing tumors showed upregulation of exhaustion markers. SOX9-mediated tumor growth advantage could not be observed in the CD8 knockout mice model, suggesting a CD8+ T cell-dependent role. In addition, SOX9 significantly upregulated collagen-related gene expression, leading to a substantial increase in collagen fibers possibly affecting the migration of immune cells into the tumor microenvironment. Together, our studies indicate that SOX9 plays a very critical role in modulating tumor microenvironment by controlling immune cell infiltration and CD8+ T cell exhaustion driving Kras G12D- driven lung tumor progression. Citation Format: Dinoop Ravindran Menon, Hua Zhong, Shridar Ganesan, Hatim Sabaawy, Sharon Pine. SOX9-dependent immune microenvironment remodeling drives KRAS-induced lung adenocarcinoma progression [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 5352.

Abstract 3952: Multiomic single-cell tumor evolution models of minimal residual disease in pediatric B-cell acute lymphoblastic leukemia

Abstract Detection of minimal residual disease (MRD) after initial diagnosis (IDX) and therapy is a strong prognostic marker for relapse in pediatric B-cell acute lymphoblastic leukemia (B-ALL). Previous studies have explored clonal genetic and gene expression changes between initial diagnosis and relapse. However, studying tumor cells present at MRD is challenging due to their low abundance. Examining the genomic profiles of leukemia cells that resist induction chemotherapy and understanding their evolutionary relationship to cells present at diagnosis may lead to a better understanding of the cells that eventually seed relapse. We analyzed somatic point mutations, indels, and copy number variants from 41 pediatric B-ALL patients with high-risk B-ALL. We specifically focused on patients from the following four common high-risk subtypes: KMT2A-rearranged, Ph+, Ph-like, and iAMP21, who had detectable disease at the end of induction. Leukemic burden at end-induction (day 29) ranged from 0.03% to 66.00%. Data were generated in partnership with the National Cancer Institute’s Human Tumor Atlas Network and the Children’s Oncology Group. Our data set of 41 patients includes 39 with paired initial diagnostic and end-induction samples that we used to create tumor evolution models based on single cells across disease stages. We produced single-cell multiome gene expression (scRNA) and chromatin accessibility (scATAC) profiles (10x Genomics) with a median of 7428 leukemic blast cells at IDX (range: 2490-14219) and 1907 at MRD (range: 240-10167) after filtering. We called somatic mutations from scRNA and scATAC using SComatic and Monopogen, and we detected copy number changes using Numbat and EpiAneufinder. We also designed a targeted DNA sequencing panel including 335 variants based on B-ALL literature and database searches to produce scDNA (Mission Bio) for 24 patients (16 IDX-MRD pairs) with a median of 2346 cells profiled at IDX (range: 340-8849) and 1828 at MRD (range: 596-5133). We built tumor phylogenies based on somatic SNVs and CNVs with COMPASS. Using multiple tools and complementary single-cell omics data types to detect somatic changes improved our confidence in the events used for tumor evolution tree construction. Our approach allowed us to define recurrent genomic events and patterns across pediatric B-ALL subtypes and to describe the clonal evolution of cells measured at initial diagnostic and end-induction disease stages. We were also able to link cell-specific mutation events to their transcriptional and epigenomic signatures, including mapping mutation events along the B-cell development trajectory and within differentially expressed gene pathways. Some cases showed dramatic shifts in subclonal structure between IDX and MRD that may be related to therapy-induced clonal selection, such as a loss of NRAS hotspot mutation. Citation Format: Steven M. Foltz, Avi Loren, Changya Chen, Rushabh Mehta, Elizabeth Li, Jason Xu, Fatemeh Alikarami, Kathrin M. Bernt, Kai Tan. Multiomic single-cell tumor evolution models of minimal residual disease in pediatric B-cell acute lymphoblastic leukemia [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 3952.

Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions.

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images.

The source code of STCellbin is available at https://github.com/STOmics/STCellbin.

A Novel, Probe-Based and Instrument-Free Method for Improved Ultrahigh-Throughput Single-Cell Gene Expression Analysis of Peripheral Blood Mononuclear Cells

Single-cell gene expression assays are critical tools for identifying functional subtypes of immune cells, as well as changes within cells resulting from pathology or treatment. However, most current methods are expensive, low in sample throughput, rely on reverse transcription and lack the sensitivity to consistently measure low expressed genes from single cells - preventing measurements of many key immune biomarkers and molecular pathways. Another problem with existing single-cell sequencing methods is that many of the cell-associated reads either fail to map to transcripts or map to uninformative ribosomal and mitochondrial genes. Some methods also require the use of live cells, which means the single-cell assay has to be carried out at the specific time of treatment or when culture is terminated. The aforementioned limitations have contributed to most single-cell sequencing studies being restricted to small numbers of samples and have prevented their wider scale adoption in biopharma, clinical, translational and applied applications. Here we report on the development and performance of a novel combinatorial split-pool-based workflow, TempO-LINC™, that adds cell-identifying molecular barcodes onto high-sensitivity gene expression probes within fixed cells. Cells can be fixed at the end of a treatment, or when obtained from a patient and stably stored for months until they are ready to be processed with other samples through the TempO-LINC assay. All probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000 cells per run. TempO-LINC simultaneously processes up to 96 fixed cell samples per run, has a simple workflow that is amenable to automation and as we will show, has a higher gene detection rate than existing single-cell platforms. We will also show that TempO-LINC has an observed doublet rate of less than 0.5% for 12,500 cells analyzed. Critically, TempO-LINC reduces sample sequencing costs and although we show the assay accurately profiles the whole transcriptome (23,000 transcripts), it can also be targeted to measure only genes/pathways of interest - dramatically reducing sequencing costs yet further. We applied TempO-LINC to assay PBMCs and a human hepatocyte model for toxicology and demonstrated the ability to correctly identify unique cell states and molecular pathways from these populations. We will also present time course and dose response data from compound treatment of cells. All of these features position TempO-LINC as a potential disruptive technology that hematology researchers, clinicians and drug developers can use for new large-scale applications/studies using single-cell sequencing across thousands of samples while obtaining improved data quality.

A PERK-Specific Inhibitor Blocks Metastatic Progression by Limiting Integrated Stress Response-Dependent Survival of Quiescent Cancer Cells.

The integrated stress response (ISR) kinase PERK serves as a survival factor for both proliferative and dormant cancer cells. We aim to validate PERK inhibition as a new strategy to specifically eliminate solitary disseminated cancer cells (DCC) in secondary sites that eventually reawake and originate metastasis. A novel clinical-grade PERK inhibitor (HC4) was tested in mouse syngeneic and PDX models that present quiescent/dormant DCCs or growth-arrested cancer cells in micro-metastatic lesions that upregulate ISR. HC4 significantly blocks metastasis, by killing quiescent/slow-cycling ISRhigh, but not proliferative ISRlow DCCs. HC4 blocked expansion of established micro-metastasis that contained ISRhigh slow-cycling cells. Single-cell gene expression profiling and imaging revealed that a significant proportion of solitary DCCs in lungs were indeed dormant and displayed an unresolved ER stress as revealed by high expression of a PERK-regulated signature. In human breast cancer metastasis biopsies, GADD34 expression (PERK-regulated gene) and quiescence were positively correlated. HC4 effectively eradicated dormant bone marrow DCCs, which usually persist after rounds of therapies. Importantly, treatment with CDK4/6 inhibitors (to force a quiescent state) followed by HC4 further reduced metastatic burden. In HNSCC and HER2+ cancers HC4 caused cell death in dormant DCCs. In HER2+ tumors, PERK inhibition caused killing by reducing HER2 activity because of sub-optimal HER2 trafficking and phosphorylation in response to EGF. Our data identify PERK as a unique vulnerability in quiescent or slow-cycling ISRhigh DCCs. The use of PERK inhibitors may allow targeting of pre-existing or therapy-induced growth arrested "persister" cells that escape anti-proliferative therapies.