Single-cell Profiling Research Articles

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Enrichment of glioblastoma tumor microenvironment in a GZMK+ T cell population.

2065 Background: Glioblastoma (GBM) bears a survival estimate below 10% at 5 years, despite standard chemoradiation treatment. Trials evaluating immune checkpoint blockade (ICB) in gliomas have been disappointing so far, dealing with the unique immunosuppressive tumor microenvironment (TME) of the brain. Nevertheless, we have clinical evidence of ICB efficacy for brain metastases (BrM) of immunosensitive tumors. Even if both GBMs and BrM share the same organ-specific microenvironment, it is clear that disease-specific immune characteristics instruct differential response to ICB. Methods: By using advanced deep learning algorithms to combine public and in-house-generated single-cell RNA sequencing profiles, we analyzed immune infiltrate characteristics of 602949 CD45+ cells from 76 GBM, 30 BrM from different types of extracranial tumors (13 NSCLC, 12 melanoma, 2 ovarian, 1 breast, 1 colorectal and 1 renal carcinoma) and 3 normal brain samples. The analysis was restricted to newly diagnosed GBM, excluding recurrent and pre-treated cases. CD8+ cells underwent segregation into clusters based on log2 normalized expression levels of CD3E and CD8A (with expression values greater than 0). The data integration utilized DESC, a deep learning model founded on autoencoders, and subsequent examination was conducted within the Scanpy framework. Cluster analysis was executed employing the Leiden algorithm. Differentially expressed genes (DEGs) were pinpointed across clusters using the Wilcoxon test, with False Discovery Rate (FDR) correction carried out through the Benjamini-Hochberg procedure. Results: From CD45+CD3+ cells, we isolated CD8+ T cells and identified 7 informative CD8+ T cells clusters. Based on DEGs, we could appreciate T cells clusters that were also recapitulated in other extracranial malignancies. The most abundant subset was composed of GZMK+ effector T cells (Cluster 0: GZMK, GZMM, CD44, KLRG1, IL7R, GZMA, CXCR3), followed by putative tumor-specific tissue-resident memory cells (Cluster 1: GZMB, LAG3, CTLA4, TIGIT, CXCL13, ITGAE, ENTPD1, PDCD1, CD27, HLA-DRB1), stem-cell memory-like T cells (Cluster 3: IL7R, CCR7, JUNB, KLF2, TCF7) and a less represented cluster characterized by high expression of genes belonging to the HSP family (Cluster 4: HSPA1, HSPH1, HSPE1, HSPD1, IFN). Interestingly, while the majority of the identified clusters was equally represented in the 3 different tissues, the GZMK+ effector T cells cluster (Cluster 0) was found to be more represented in GBM compared to BrM and normal brain tissue. Conclusions: A GZMK+ effector T cells subpopulation, specifically enriched in GBM, has been poorly characterized in its function and spatial localization. Our data highlight the importance of studying the composition of the immune infiltrate in GBM with multi-omics approaches in order to uncover deep mechanisms of resistance to ICB and eventually provide hints on potential therapeutic targets.

Characterizing the tumor and immune landscape of patients with melanoma treated with combined checkpoint blockade and MAPK-targeted therapy.

e21503 Background: Melanoma therapy has been revolutionized by two novel therapeutic approaches: mitogen activated protein kinase (MAPK) targeted therapy (MTT) and immune checkpoint blockade therapy (ICB). It has been shown that MTT enhances anti-tumor immunity within the tumor microenviroment (TME), thus providing a strong rationale for its combination with immunotherapy. Regimens combining MTT with ICB there have had mixed results, and which patients (pts) should be treated with these combinations is unknown. Methods: 16 pts harboring BRAFV600 mutation were enrolled and treated with 2 weeks (wks) of MTT (dabrafenib plus trametinib) then 6 wks of concomitant MTT and pembrolizumab, followed by single-agent pembrolizumab. The primary endpoint was clinical benefit (CB) defined as partial/complete response or stable disease (per RECIST1.1) persisting at 24 wks. Serial biopsies were performed prior to MTT, following the 2-week lead-in of MTT, and following 6 wks of combination immune therapy. Single-cell RNA-seq profiling of CD45+ and CD45- cells was performed using both the smart-seq2 plate-based protocol and 10x genomics platform. Results: Out of the 16 pts, 14 received both MTT and ICB, of them only 5 pts had CB. 2 pts did not receive ICB due to MTT toxicity. A clustering analysis of all immune cells showed a significant recruitment of Cytotoxic CD8+ T cells after MTT treatment in pts with CB. In addition T cell clusters had a significantly higher expression of IFNG pathway genes at both baseline and after MTT compared to NCB pts. Tumor associated macrophages (TAM) in NCB pts possessed mainly a “M2” phenotype and expressed a significantly higher level of immune suppressor genes, such as HLA-G, PLAC8 and CD52. In contrast, most of the TAMs occupying the tumor of CB pts had M1 phenotype, and significantly expressed APOE, IL10, CXC12. Calculation of M1/M2 score correlated with chemokines expressed by tumor cells such as CCL3 and TIGIT. In addition, MAPK-pathway genes expressed by the tumor cells were down-regulated after MTT in both CB & NCB pts, and up-regulated after ICB, but only in NCB pts. Our analysis also revealed tumor programs that were significantly higher in NCB patients: the first expressing high MITF-SOX levels pre-therapy that decreased after MTT, the second expressing AXL, low at baseline and up regulated after combination treatment. These AXL expressing cells also had a significantly higher TGF-β expression levels. Moreover, MITF/AXL ratio in NCB patients decreased significantly after MTT and after ICB. Conclusions: Overall, abbreviated MTT with ICB did not lead to increased CB. Our findings highlights factors that modify the TME towards immune suppression, tumor programs resistance, MAPK up-regulation and can thus enhance drug resistance. Clinical trial information: NCT03149029 .

Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM.

Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation. In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset. Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets. Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.

A hybrid epithelial/mesenchymal state in head and neck cancer: A biomarker for survival with differential prognosis by self-reported race

Head and neck squamous cell carcinoma (HNSCC) is the 6th leading cause of cancer-related mortality, with racial disparities amplifying the challenges in treatment. Although the relationship between hybrid epithelial/mesenchymal (E/M) states and tumor progression is of interest, no studies have characterized the clinical relevance of hybrid E/M states in head and neck cancer outcomes among self-reported racial cohorts. Given the overlap in gene expression between hybrid E/M malignant cells and cancer-associated fibroblasts, we utilized deconvolution of bulk RNA sequencing data from oral cavity and laryngeal squamous cell carcinoma tumors from The Cancer Genome Atlas. We utilized our previously collected single-cell profiles to generate inferred malignant profiles and then scored these for hybrid E/M. We then conducted a survival analysis on overall and disease-free survival among self-reported Black and White Americans. The hybrid E/M state was differentially associated with head and neck cancer survival by self-reported race and ethnicity, with a stronger association in non-Hispanic Black patients. Black patients with a high hybrid E/M score had a higher risk of death or recurrence (hazard ratio [HR]: 4.18 [95% confidence interval (CI): 2.06, 8.49]) than White patients with a high hybrid E/M score (HR: 1.58 [95% CI: 1.11, 2.26]). Our results suggest a complex interplay of social structure, racism, and genetic diversity. We implore researchers to consider the social and biological context contributing to disparities. A.L.M. received support from the National Institute of Minority Health and Health Disparities (K01MD013897 [principal investigator (PI), A.L.M.]). S.V.P. received support from the National Institute of Dental and Craniofacial Research (R01DE032865 [PI, S.V.P.] and R01DE032371 [PI, S.V.P.]).

Unveiling gene regulatory networks during cellular state transitions without linkage across time points

Time-stamped cross-sectional data, which lack linkage across time points, are commonly generated in single-cell transcriptional profiling. Many previous methods for inferring gene regulatory networks (GRNs) driving cell-state transitions relied on constructing single-cell temporal ordering. Introducing COSLIR (COvariance restricted Sparse LInear Regression), we presented a direct approach to reconstructing GRNs that govern cell-state transitions, utilizing only the first and second moments of samples between two consecutive time points. Simulations validated COSLIR’s perfect accuracy in the oracle case and demonstrated its robust performance in real-world scenarios. When applied to single-cell RT-PCR and RNAseq datasets in developmental biology, COSLIR competed favorably with existing methods. Notably, its running time remained nearly independent of the number of cells. Therefore, COSLIR emerges as a promising addition to GRN reconstruction methods under cell-state transitions, bypassing the single-cell temporal ordering to enhance accuracy and efficiency in single-cell transcriptional profiling.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data.

Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model. While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200K cells) and bulk TCGA glioma data (n = 711 samples). Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

Single-Cell Profiling Reveals the Impact of Genetic Alterations on the Differentiation of Inflammation-Induced Murine Colon Tumors.

Genetic mutations and chronic inflammation of the colon contribute to the development of colorectal cancer (CRC). Using a murine model of inflammation-induced colon tumorigenesis, we determined how genetic mutations alter colon tumor cell differentiation. Inflammation induced by enterotoxigenic Bacteroides fragilis (ETBF) colonization of multiple intestinal neoplasia (MinApcΔ716/+) mice triggers loss of heterozygosity of Apc causing colon tumor formation. Here, we report that the addition of BRAFV600E mutation (BRAFF-V600ELgr5tm1(Cre/ERT2)CleMinApcΔ716/+, BLM) or knocking out Msh2 (Msh2LoxP/LoxPVil1-creMinApcΔ716/+, MSH2KO) in the Min model altered colon tumor differentiation. Using single-cell RNA sequencing, we uncovered the differences between BLM, Min, and MSH2KO tumors at a single-cell resolution. BLM tumors showed an increase in differentiated tumor epithelial cell lineages and a reduction in the tumor stem cell population. Interestingly, the tumor stem cell population of BLM tumors had revival colon stem cell characteristics with low WNT signaling and an increase in RevCSC marker gene expression. In contrast, MSH2KO tumors were characterized by an increased tumor stem cell population that had higher WNT signaling activity compared to Min tumors. Furthermore, overall BLM tumors had higher expression of transcription factors that drive differentiation, such as Cdx2, than Min tumors. Using RNA velocity, we identified additional potential regulators of BLM tumor differentiation such as NDRG1. The role of CDX2 and NDRG1 as putative regulators for BLM tumor cell differentiation was verified using organoids derived from BLM tumors. Our results demonstrate the critical connections between genetic mutations and cell differentiation in inflammation-induced colon tumorigenesis. Understanding such roles will deepen our understanding of inflammation-associated colon cancer.

Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts

Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs' deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p= 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p= 0.032, OMIM genes: adjusted p<0.0002), while only DNV genes were enriched in the endothelium (adjusted p= 0.032). Although we did not achieve significant findings using CPO gene sets, which was mainly due to the limited number of DNV genes, scRNA-seq analysis implicated various expression patterns among DNV and OMIM genes. Our results suggest that combinatory pathway and scRNA-seq data analyses are helpful for contextualizing genes in OFC development.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in DSRCTs

Desmoplastic small round cell tumor (DSRCT) is a rare, aggressive sarcoma driven by the EWSR1::WT1 chimeric transcription factor. Despite this unique oncogenic driver, DSRCT displays a polyphenotypic differentiation of unknown causality. Using single-cell multi-omics on 12 samples from five patients, we find that DSRCT tumor cells cluster into consistent subpopulations with partially overlapping lineage- and metabolism-related transcriptional programs. Invitro modeling shows that high EWSR1::WT1 DNA-binding activity associates with most lineage-related states, in contrast to glycolytic and profibrotic states. Single-cell chromatin accessibility analysis suggests that EWSR1::WT1 binding site variability may drive distinct lineage-related transcriptional programs, supporting some level of cell-intrinsic plasticity. Spatial transcriptomics reveals that glycolytic and profibrotic states specifically localize within hypoxic niches at the periphery of tumor cell islets, suggesting an additional role of tumor cell-extrinsic microenvironmental cues. We finally identify a single-cell transcriptomics-derived epithelial signature associated with improved patient survival, highlighting the clinical relevance of our findings.

A single-cell multiomics profile of Fontan-associated liver disease.

A single-cell multiomics profile of Fontan-associated liver disease.

Single-cell analysis of the survival mechanisms of fratricidal CAR-T targeting of T cell malignancies

Chimeric antigen receptor T (CAR-T) cell therapy targeting T-cell tumors still faces many challenges, one of which is its fratricide due to the target gene expressed on CAR-T cells. Despite this, these CAR-T cells can be expanded in vitro via extending culture time and effectively eliminating malignant T cells. However, the mechanisms underlying CAR-T cell survival from cell subpopulations, molecules and regulation are still unknown. We performed single-cell transcriptome profiling to investigate the fratricidal CAR-T products (CD26 CAR-T and CD44v6 CAR-T) targeting T cells, taking CD19 CAR-T targeting B cells from the same donor as a control. Compared with CD19 CAR-T, fratricidal CAR-T cells exhibit no unique cell subpopulation, but have more exhausted T cells, fewer cytotoxic T cells and more TCR clonal amplification. Furthermore, we observed that fratricidal CAR-T cell survival was accompanied by target gene expression. Gene expression results suggests that fratricidal CAR-T cells may downregulate their HLA molecules to evade T-cell recognition. Single-cell regulatory network analysis and suppression experiments revealed that exhaustion mediated by critical regulatory factors may contribute to fratricidal CAR-T cell survival. Together, these data provide valuable and first-time insights into the survival of fratricidal CAR-T cells.

P33 Skinvaders: gamification of the skin barrier to engage school-age children in the biology of the skin

Abstract Introduction and aims Public engagement is an essential part of research design. It provides opportunities for both scientists and the public to learn, and inspires the next generation of researchers. We are fortunate to have a public engagement centre within our institution, which contains digital games, an established and effective way to engage children. We developed a new game for school-age children to engage them in the Paediatric Human Cell Atlas, a collaborative project that aims to map single-cell profiles and spatial characterization of gene expression across human tissues and organs throughout development. Our aim was to develop a game that involved different skin cell types, variation in skin colour and common skin disorders. Methods Initial steps included brainstorming with our public engagement staff about content and then developing a lay summary and agreeing on the following key themes: the skin as a barrier, the cells of the skin, and eczema, a common skin disorder. The game was based on the arcade game Breakout. It features a customizable paddle representing the skin, demonstrating how changes in quantities of different skin components can change appearance/function (e.g. melanin production, adipocytes, hair follicles), which players must move across the screen to keep pathogens and allergens out and keep water and protein in. In the last level, barrier function is impaired by eczema and the children can catch cream to repair the barrier. Results The game was developed for our public engagement centre where it has undergone testing by target audiences. Conclusions We have created a game as an innovative way to engage and educate paediatric communities in our research. It was challenging to simplify scientific content for use in a game while staying true to the scientific concepts. We will increase reach by adapting the game for desktop and app-based formats.

Crosstalk among proximal tubular cells, macrophages, and fibroblasts in acute kidney injury: single-cell profiling from the perspective of ferroptosis

The link between ferroptosis, a form of cell death mediated by iron and acute kidney injury (AKI) is recently gaining widespread attention. However, the mechanism of the crosstalk between cells in the pathogenesis and progression of acute kidney injury remains unexplored. In our research, we performed a non-negative matrix decomposition (NMF) algorithm on acute kidney injury single-cell RNA sequencing data based specifically focusing in ferroptosis-associated genes. Through a combination with pseudo-time analysis, cell–cell interaction analysis and SCENIC analysis, we discovered that proximal tubular cells, macrophages, and fibroblasts all showed associations with ferroptosis in different pathways and at various time. This involvement influenced cellular functions, enhancing cellular communication and activating multiple transcription factors. In addition, analyzing bulk expression profiles and marker genes of newly defined ferroptosis subtypes of cells, we have identified crucial cell subtypes, including Egr1 + PTC-C1, Jun + PTC-C3, Cxcl2 + Mac-C1 and Egr1 + Fib-C1. All these subtypes which were found in AKI mice kidneys and played significantly distinct roles from those of normal mice. Moreover, we verified the differential expression of Egr1, Jun, and Cxcl2 in the IRI mouse model and acute kidney injury human samples. Finally, our research presented a novel analysis of the crosstalk of proximal tubular cells, macrophages and fibroblasts in acute kidney injury targeting ferroptosis, therefore, contributing to better understanding the acute kidney injury pathogenesis, self-repairment and acute kidney injury-chronic kidney disease (AKI-CKD) progression.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70–80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Single-Cell Transcriptomic Profiling Unveils Dynamic Immune Cell Responses during Haemonchus contortus Infection.

Haemonchus contortus is a parasite widely distributed in tropical, subtropical, and warm temperate regions, causing significant economic losses in the livestock industry worldwide. However, little is known about the genetics of H. contortus resistance in livestock. In this study, we monitor the dynamic immune cell responses in diverse peripheral blood mononuclear cells (PBMCs) during H. contortus infection in goats through single-cell RNA sequencing (scRNA-Seq) analysis. A total of four Boer goats, two goats with oral infection with the L3 larvae of H. contortus and two healthy goats as controls, were used in the animal test. The infection model in goats was established and validated by the fecal egg count (FEC) test and qPCR analysis of the gene expression of IL-5 and IL-6. Using scRNA-Seq, we identified seven cell types, including T cells, monocytes, natural killer cells, B cells, and dendritic cells with distinct gene expression signatures. After identifying cell subpopulations of differentially expressed genes (DEGs) in the case and control groups, we observed the upregulation of multiple inflammation-associated genes, including NFKBIA and NFKBID. Kyoto Encyclopedia of the Genome (KEGG) enrichment analysis revealed significant enrichment of NOD-like receptor pathways and Th1/Th2 cell differentiation signaling pathways in CD4 T cells DEGs. Furthermore, the analysis of ligand-receptor interaction networks showed a more active state of cellular communication in the PBMCs from the case group, and the inflammatory response associated MIF-(CD74 + CXCR4) ligand receptor complex was significantly more activated in the case group, suggesting a potential inflammatory response. Our study preliminarily revealed transcriptomic profiling characterizing the cell type specific mechanisms in host PBMCs at the single-cell level during H. contortus infection.

Identification of a clinically efficacious CAR T cell subset in diffuse large B cell lymphoma by dynamic multidimensional single-cell profiling.

Chimeric antigen receptor (CAR) T cells used for the treatment of B cell malignancies can identify T cell subsets with superior clinical activity. Here, using infusion products of individuals with large B cell lymphoma, we integrated functional profiling using timelapse imaging microscopy in nanowell grids with subcellular profiling and single-cell RNA sequencing to identify a signature of multifunctional CD8+ T cells (CD8-fit T cells). CD8-fit T cells are capable of migration and serial killing and harbor balanced mitochondrial and lysosomal volumes. Using independent datasets, we validate that CD8-fit T cells (1) are present premanufacture and are associated with clinical responses in individuals treated with axicabtagene ciloleucel, (2) longitudinally persist in individuals after treatment with CAR T cells and (3) are tumor migrating cytolytic cells capable of intratumoral expansion in solid tumors. Our results demonstrate the power of multimodal integration of single-cell functional assessments for the discovery and application of CD8-fit T cells as a T cell subset with optimal fitness in cell therapy.