Single-cell Expression Profiling Research Articles

Single-cell RNA-seq analysis reveals the Wnt/Ca2+ signaling pathway with inflammation, apoptosis in nucleus pulposus degeneration

BackgroundIncreasing studies have shown degeneration of nucleus pulposus cells (NPCs) as an critical part of the progression of intervertebral disc degeneration (IVDD). However, there are relatively few studies on single-cell transcriptome contrasts in human degenerated NPCs. Moreover, differences in Wnt/Ca2+ signaling in human degenerated nucleus pulposus cells have not been elucidated. The aim of this study is to investigate the differential expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans and try to investigate its mechanism.MethodsWe performed bioinformatics analysis using our previously published findings to construct single cell expression profiles of normal and degenerated nucleus pulposus. Then, in-depth differential analysis was used to characterize the expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans.ResultsThe obtained cell data were clustered into five different chondrocytes clusters, which chondrocyte 4 and chondrocyte 5 mainly accounted for a high proportion in degenerated nucleus pulposus tissues, but rarely in normal nucleus pulposus tissues. Genes associated within the Wnt/Ca2+ signaling pathway, such as Wnt5B, FZD1, PLC (PLCB1), CaN (PPP3CA) and NAFATC1 are mainly present in chondrocyte 3, chondrocyte 4 and chondrocyte 5 from degenerated nucleus pulposus tissues. In addition, as a receptor that activates Wnt signaling pathway, LRP5 is mainly highly expressed in chondrocyte 5 of degenerated nucleus pulposus cells. Six genes, ANGPTL4, PTGES, IGFBP3, GDF15, TRIB3 and TNFRSF10B, which are associated with apoptosis and inflammatory responses, and are widespread in chondrocyte 4 and chondrocyte 5, may be closely related to degenerative of nucleus pulposus cells.ConclusionsSingle-cell RNA sequencing revealed differential expression of Wnt/Ca2+ signaling in human normal and degenerated nucleus pulposus cells, and this differential expression may be closely related to the abundance of chondrocyte 4 and chondrocyte 5 in degenerated nucleus pulposus cells. In degenerated nucleus pulposus cells, LRP5 activate Wnt5B, which promotes nucleus pulposus cell apoptosis and inflammatory response by regulating the Wnt/Ca2+ signaling pathway, thereby promoting disc degeneration. ANGPTL4, IGFBP3, PTGES in chondrocyte 4 and TRIB3, GDF15, TNFRSF10B in chondrocyte 5 may play an important role in this process.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Xenografted human microglia display diverse transcriptomic states in response to Alzheimer’s disease-related amyloid-β pathology

Microglia are central players in Alzheimer’s disease pathology but analyzing microglial states in human brain samples is challenging due to genetic diversity, postmortem delay and admixture of pathologies. To circumvent these issues, here we generated 138,577 single-cell expression profiles of human stem cell-derived microglia xenotransplanted in the brain of the AppNL-G-F model of amyloid pathology and wild-type controls. Xenografted human microglia adopt a disease-associated profile similar to that seen in mouse microglia, but display a more pronounced human leukocyte antigen or HLA state, likely related to antigen presentation in response to amyloid plaques. The human microglial response also involves a pro-inflammatory cytokine/chemokine cytokine response microglia or CRM response to oligomeric Aβ oligomers. Genetic deletion of TREM2 or APOE as well as APOE polymorphisms and TREM2R47H expression in the transplanted microglia modulate these responses differentially. The expression of other Alzheimer’s disease risk genes is differentially regulated across the distinct cell states elicited in response to amyloid pathology. Thus, we have identified multiple transcriptomic cell states adopted by human microglia in a multipronged response to Alzheimer’s disease-related pathology, which should be taken into account in translational studies.

Abstract 3770: Comparative analysis of single cell expression and digital spatial profiling in FFPE tumor tissues

Abstract Formalin Fixed Paraffin Embedded (FFPE) tissues are a staple in clinical diagnostics associated with solid tumor. Next Generation Sequencing (NGS) approaches characterize genomes, epigenomes, transcriptomes, and proteomes from a single sample source. FFPE blocks from several different tumor tissue types were serially sliced into FFPE slides. Individual slides were then utilized to explore the genome, epigenome, single cell RNA-Seq (scRNA-Seq), and Digital Spatial Profiling (DSP). Here we present a comparative analysis of two transcriptomic modalities within one FFPE tissue sample: fixed-cell scRNA-Seq of the transcriptome and digital spatial profiling of the whole transcriptome. Independent analysis of scRNA-Seq and DSP identified common trends as well as distinct differences within the same sample. Comparative analysis within sample between modalities identified statistically significant differences, driven by local tumor microenvironment, but also highlighted commonalities between methods. Cross sample comparisons showed striking differences, as indicated by tumor type. These studies highlight the strengths of each assay individually, as well as the power of a dual-assay approach for comprehensive analysis of the tumor and its microenvironment. Conclusions from this standalone analysis were further aggregated into downstream multiomics analyses, including proteomics analysis, illustrating the expanding landscape of information that can be extracted from FFPE derived tissue and the potential for novel discovery and diagnostic power integrating these complex data types holds. Citation Format: Anthony Moore, Andrea O'Hara, Pranay Vishwanath, Laure Turner, Haythem Latif, Chris Mozdzierz, Ginger Zhou. Comparative analysis of single cell expression and digital spatial profiling in FFPE tumor tissues [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 3770.

Abstract 103: Prostate cancer bone metastasis: Therapeutic targeting of tumor associated macrophages

Abstract Introduction: Castration-resistant bone metastatic prostate cancer is a morbid and deadly disease with an exceedingly low response rates to available immunotherapies. Understanding the mechanisms behind immune evasion and tumor progression is critical to improving therapy. Historically, bone metastases have been challenging to model in the laboratory. Our foundational work was built upon fresh patient samples taken directly from the operating room for analysis across multiple platforms: IHC, single cell expression profiling, and spatial transcriptomics. We compared tumors from patients with bone metastatic disease to tumors from patients with disease localized to the prostate. Our work (Cancer Cell, 2021 and Nature Communications, 2023) has consistently pointed towards dysregulated myeloid populations within the tumor microenvironment, particularly TREM2+ tumor associated macrophages (TAMs), as key drivers of disease progression. Methods: Our patient samples and murine pre-clinical models of prostate cancer highlighted several specifically upregulated molecules and pathways in TAMs. Here we focus on TAMs in vitro and in vivo using a new and genetically tractable myeloid model to interrogate the mechanistic role of key pathways, and how they may contribute to T-cell immune suppression. The ER-Hoxb8 system of conditional myeloid differentiation permitted the generation of isogenic cell lines to interrogate the roles of key molecules TREM2, MSR1, APOE, and MERTK through overexpression or CRISPR/Cas9 knockout. Results: Using a system of co-culture in vitro, we quantified the role of these proteins in suppressing T-cell proliferation and cytokine production, as well as their role in TAM differentiation and function. In vivo, these lines were co-injected subcutaneously with syngeneic RM1-BM3 tumor cells to quantify their effect on tumor growth and T-cell suppression in vivo. In addition to localized subcutaneous tumors, bone metastatic disease was established by intracardiac injection. Furthermore, established tumors were treated with TAM-directed small molecule therapy (e.g., MERTK inhibition) as well as immune checkpoint inhibition (anti-PD1) alone, and in combination. Conclusions: TAMs are immune suppressive myeloid cells that are not found in normal tissues. They are specifically recruited to tumors and highly enriched in bone metastatic disease. Transcriptomic analyses highlighted signaling pathways potentially amenable to productive therapeutic targeting. The ER-Hoxb8 system was highly effective in generating a genetically tractable model of TAM for study in vitro and in vivo. Our work supports the hypothesis that the specific targeting of TAMs, by focusing on critical signaling pathways, can be effective in the treatment of pre-clinical models of prostate cancer. This new approach merits further study as a potential strategy for the treatment of patients with bone metastases. Citation Format: Hanyu Zhang, Shenglin Mei, Nathan E. Jeffries, Douglas M. Dahl, Chin-Lee Wu, Philip J. Saylor, David B. Sykes. Prostate cancer bone metastasis: Therapeutic targeting of tumor associated macrophages [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 103.

Abstract 7329: Novel MAVE models for MLH1, MSH2, and PMS2 have high accuracy

Abstract Lynch syndrome, the most common cause of hereditary colorectal cancer, is due to pathogenic germline variants in mismatch repair (MMR) genes. Variants of uncertain significance (VUS) in MMR genes hinder diagnoses and clinical management. Multiplex assays of variant effects (MAVEs) are high-throughput cellular assays designed to provide functional data on variant activity. Successful MAVE experiments accurately define the pathogenicity of single nucleotide variants and small insertions-deletions which can then be applied to clinical variant classification (VC). Here we report internally generated MAVE datasets for three MMR genes (MSH2, MLH1 and PMS2) and estimate their impact on patients. Three separate pools of targeted variants were generated for three MMR genes. Pooled variants were introduced into cell populations such that each cell contained one variant copy of an MMR gene. Single-cell RNA expression profiles were generated for the cells which were then normalized and filtered. Supervised machine learning was used to find patterns in gene expression that were different between cells harboring established pathogenic (P) and benign (B) variants and to assess the accuracy of variant predictions. VC impact was estimated for all datasets by simulating VCs with the added MAVE evidence compared to the original VCs. Variants with an original VC that was different after MAVE evidence were counted as impacted. Overall results are shown in Table 1, including the number of variants successfully targeted, the number of known P and B variants used for training and testing, the area under the receiver operating characteristic curve (AUROC), the distribution of calibrated predictions, and the estimated patient impact for each model. Table 1. Gene MSH2 model MLH1 model PMS2 model Variants successfully targeted (n) 223 212 214 Known pathogenic (P) and benign (B) variants used to train the model 29 P 31 P 14 P 72 B 55 B 71 B Known pathogenic (P) and benign (B) variants used to test the model 14 P 14 P 7 P 32 B 22 B 31 B AUROC 0.9996 0.9642 0.9918 # of variants strongly predicted P (PPV ≥ 95%) or B (NPV≥ 95%) 44 P 52 P 26 P 177 B 160 B 183 B # of variants with uncertain prediction (PPV < 80%, NPV < 80%) 2 0 5 Estimated # of impacted variants 4 LP>P 0 LP>P 4 LP>P 3 VUS>LP 5 VUS>LP 1 VUS>LP 26 VUS>LB 17 VUS>LB 17 VUS>LB 31 LB>B 7 LB>B 6 LB>B The MSH2, MLH1, and PMS2 MAVE models provide a high-quality source of functional evidence for VC with the potential to impact many patients in the future. Citation Format: Wolfgang Michael Korn, Samskruthi Padigepati, David Stafford, Flavia Facio, Britt Johnson, Keith Nykamp, Jason Reuter. Novel MAVE models for MLH1, MSH2, and PMS2 have high accuracy [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 7329.

Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology

Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.

Exploration of the prognostic prediction value of the PANoptosis-based risk score and its correlation with tumor immunity in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common cancer with high mortality worldwide. PANoptosis is a novel inflammatory programmed cell death modality with the characteristics of pyroptosis, apoptosis and necroptosis. It is necessary to explore PANoptosis-related genes in LUAD patients and offer evidence for prognosis prediction and therapeutic strategies. Single-cell RNA sequencing data and RNA expression profiles of LUAD patients from The Cancer Genome Atlas and Gene Expression Omnibus databases are used to screen PANoptosis-related differential genes for the construction of a risk model. Fifteen PANoptosis-related markers with prognostic value were identified byLeast Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis. Kaplan-Meier analysis and receiver operating characteristic curve analysis further demonstrated the significant predictive capability. Immune infiltration, Single Nucleotide Variants (SNV) mutations, and clinical drug susceptibility were analyzed. In conclusion, a risk model of 15 PANoptosis-related genes has significant value in prognostic prediction for LUAD and has potential to direct clinical therapeutic strategies during the treatment.

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.

Comprehensive analysis of genetic associations and single-cell expression profiles reveals potential links between migraine and multiple diseases: a phenome-wide association study.

Migraine is a common neurological disorder that affects more than one billion people worldwide. Recent genome-wide association studies have identified 123 genetic loci associated with migraine risk. However, the biological mechanisms underlying migraine and its relationships with other complex diseases remain unclear. We performed a phenome-wide association study (PheWAS) using UK Biobank data to investigate associations between migraine and 416 phenotypes. Mendelian randomization was employed using the IVW method. For loci associated with multiple diseases, pleiotropy was tested using MR-Egger. Single-cell RNA sequencing data was analyzed to profile the expression of 73 migraine susceptibility genes across brain cell types. qPCR was used to validate the expression of selected genes in microglia. PheWAS identified 15 disorders significantly associated with migraine, with one association detecting potential pleiotropy. Single-cell analysis revealed elevated expression of seven susceptibility genes (including ZEB2, RUNX1, SLC24A3, ANKDD1B, etc.) in brain glial cells. And qPCR confirmed the upregulation of these genes in LPS-treated microglia. This multimodal analysis provides novel insights into the link between migraine and other diseases. The single-cell profiling suggests the involvement of specific brain cells and molecular pathways. Validation of gene expression in microglia supports their potential role in migraine pathology. Overall, this study uncovers pleiotropic relationships and the biological underpinnings of migraine susceptibility.

Biological basis of extensive pleiotropy between blood traits and cancer risk.

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations. Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis. The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis. This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

Integrating single-cell and bulk expression data to identify and analyze cancer prognosis-related genes

Compared with traditional evaluation methods of cancer prognosis based on tissue samples, single-cell sequencing technology can provide information on cell type heterogeneity for predicting biomarkers related to cancer prognosis. Therefore, the bulk and single-cell expression profiles of breast cancer and normal cells were comprehensively analyzed to identify malignant and non-malignant markers and construct a reliable prognosis model. We first screened highly reliable differentially expressed genes from bulk expression profiles of multiple breast cancer tissues and normal tissues, and inferred genes related to cell malignancy from single-cell data. Then we identified eight critical genes related to breast cancer to conduct Cox regression analysis, calculate polygenic risk score (PRS), and verify the predictive ability of PRS in two data groups. The results show that PRS can divide breast cancer patients into high-risk group and low-risk group. PRS is related to the overall survival time and relapse-free interval and is a prognosis factor independent of conventional clinicopathological characteristics. Breast cancer is usually regarded as a cancer with a relatively good prognosis. In order to further explore whether this workflow can be applied to cancer with poor prognosis, we selected lung cancer for a comparative study. The results show that this workflow can also build a reasonable prognosis model for lung cancer. This study provides new insight and practical source code for further research on cancer biomarkers and drug targets. It also provides basis for survival prediction, treatment response prediction, and personalized treatment.

ScPerturb: harmonized single-cell perturbation data.

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.

A mucus production programme promotes classical pancreatic ductal adenocarcinoma

ObjectiveThe optimal therapeutic response in cancer patients is highly dependent upon the differentiation state of their tumours. Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer that harbours distinct phenotypic subtypes...

Combination of blockade of endothelin signalling and compensation of IGF1 expression protects the retina from degeneration.

Retinitis pigmentosa (RP) and macular dystrophy (MD) cause severe retinal dysfunction, affecting 1 in 4000 people worldwide. This disease is currently assumed to be intractable, because effective therapeutic methods have not been established, regardless of genetic or sporadic traits. Here, we examined a RP mouse model in which the Prominin-1 (Prom1) gene was deficient and investigated the molecular events occurring at the outset of retinal dysfunction. We extracted the Prom1-deficient retina subjected to light exposure for a short time, conducted single-cell expression profiling, and compared the gene expression with and without stimuli. We identified the cells and genes whose expression levels change directly in response to light stimuli. Among the genes altered by light stimulation, Igf1 was decreased in rod photoreceptor cells and astrocytes under the light-stimulated condition. Consistently, the insulin-like growth factor (IGF) signal was weakened in light-stimulated photoreceptor cells. The recovery of Igf1 expression with the adeno-associated virus (AAV) prevented photoreceptor cell death, and its treatment in combination with the endothelin receptor antagonist led to the blockade of abnormal glial activation and the promotion of glycolysis, thereby resulting in the improvement of retinal functions, as assayed by electroretinography. We additionally demonstrated that the attenuation of mammalian/mechanistic target of rapamycin (mTOR), which mediates IGF signalling, leads to complications in maintaining retinal homeostasis. Together, we propose that combinatorial manipulation of distinct mechanisms is useful for the maintenance of the retinal condition.

Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer.

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.

Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA.

Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.

Molecularly defined and spatially resolved cell atlas of the whole mouse brain

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1–3, including several brain regions (for example, refs. 1–11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand–receptor) basis and functional implications of these cell–cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.

Predicting drug response from single-cell expression profiles of tumours

BackgroundIntra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.ResultsDrug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.ConclusionsDREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.

Distinct Minor Neoplastic Follicular T-Cell Subsets Dictate Lymphoma Fates through Functional Specialization

Background: The presence and role of follicular T-cell populations other than T follicular helper (Tfh) cells, such as T follicular regulatory (Tfr) and cytotoxic (Tfc) cells, are gaining increasing attention in certain pathological states. However, the ecosystem of follicular T cells in the tumor microenvironment (TME) has not been fully elucidated. In particular, the significance of minor follicular T-cell subsets in the neoplastic follicular environment remains elusive. Here, we aimed to reveal the landscape of follicular T-cell alterations in various cancers, with a particular emphasis on the follicular lymphoma (FL) TME. Methods: We analyzed single-cell RNA/TCR sequencing data of &amp;gt;500,000 human T cells from FL (obtained from four cohorts) and 25 other cancer types, as well as homeostatic and reactive lymph nodes (LNs), to construct a comprehensive single-T-cell atlas. We investigated differentially expressed genes, RNA velocity, and TCR clonality using this atlas. To determine the functions of neoplastic follicular regulatory (Tnfr) and cytotoxic (Tnfc) T cells, we performed in vitro cytokineproduction and co-culture assays, in combination with cell activation/suppression, cell division, and apoptosis assays, using human FL samples. With the PhenoCycler-Fusion system, we conducted multiplex digital spatial profiling (DSP) of 169 FL samples from two independent cohorts (now being extended to 242 FL samples from three cohorts) for &amp;gt;25 antibodies. We also performed single-cell spatial and protein expression profiling and prognostic analysis. Results: In FL, distinct minor neoplastic follicular T-cell subsets-Tnfr and CD4 (Tnfc4) and CD8 (Tnfc8) Tnfc cells-increased relative to homeostatic LNs. The TCR repertoire analysis revealed that Tnfr cells shared clonotypes with conventional effector regulatory T (Trg) and Tfh cells, whereas Tnfc4 and Tnfc8 cells shared clonotypes with Tfh cells and effector and exhausted (Tcex) cytotoxic CD8 T cells, respectively. In line with these findings, the RNA velocity survey suggested that Tnfr, Tnfc4, and Tnfc8 cells originated from Trg, Tfh, and naïve-like CD8 T cells, respectively. Tnfr and Tnfc cells expressed higher levels of effector genes, including those involved in cytokine release, chemokine response, migration, and PD-1 signaling, than their reactive LN counterparts. The pan-cancer survey revealed that Tfr and CD4 Tfc cells were exclusive to FL, whereas the prevalence and gene expression profiles of CD8 Tfc cells varied across cancers. Tnfr cells were marked by abundant expression of IL10 and IL21, whereas Tnfc cells displayed a unique phenotype, as they concomitantly expressed markers of effector Tfh (e.g., CXCL13, CXCR5, and PDCD1), naïve/stem (e.g., CCR7 and TCF7), central memory (e.g., CD27, CD28, and SELL), and tissue-resident memory (e.g., ITGAE) cells. Hierarchical clustering demonstrated that Tnfc8 cells had transcriptional profiles similar to those of melanoma TCF1 +PD-1 +CD8 + stem-like T cells. DSP of FL detected Tnfr and Tnfc cells frequently localized within and around neoplastic follicles, forming a cellular neighborhood that allowed them to interact closely. Tnfr cells were distributed predominantly near Tfh cells. The functional co-culture assays demonstrated that Tnfr cells suppressed Tfh-cell activation and division, thereby inhibiting Tfh-mediated malignant B-cell activation and survival. Tnfc8 cells showed a higher cell division capability than that of Tcex cells, suggesting that Tnfc8 cells function as a pool of CD8 T cells in neoplastic follicles. The prognostic analysis revealed that Tnfr and Tnfc cell proportions correlated with early disease relapse (i.e., POD24) and predicted a significantly longer time-to-relapse ( P &amp;lt;0.05 for Tnfr and &amp;lt;0.001 for Tnfc cells) in FL. In the multivariate analysis, the prognostic impact of these two cell subsets was independent of the FLIPI. The prognostic analysis findings were confirmed using a validation cohort. Conclusions: Our multi-omics approach identified the expansion of minor neoplastic follicular T-cell subsets that carry unique transcriptional and functional profiles and robust prognostic impacts. These findings deepen our understanding of the biological and immunological roles of non-Tfh follicular T cells in the lymphoma TME and highlights their clinical potential for patient risk stratification and future therapeutic interventions.