Single-cell Expression Research Articles

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

Identification of novel protein biomarkers and therapeutic targets for ankylosing spondylitis using human circulating plasma proteomics and genome analysis.

The proteome serves as the primary basis for identifying targets for treatment. This study conducted proteomic range two-sample Mendelian randomization (MR) analysis to pinpoint potential protein markers and treatment targets for ankylosing spondylitis (AS). A total of 4907 data points on circulating protein expression were collected from a large-scale protein quantitative trait locus investigation involving 35,559 individuals. Using data from a Finnish study on AS as the outcome, the dataset comprised 166,144 individuals of European ancestry (1462 cases and 164,682 controls), and causal relationships were determined through bidirectional Mendelian randomization of two samples. Proteins were further validated and identified through single-cell expression analysis, certain cells showing enriched expression levels were detected, and possible treatment targets were optimized. Increased HERC5 expression predicted by genes was related to increased AS risk, whereas the expression of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, was negatively correlated with AS risk. For each increase in gene-predicted protein levels, the ORs of AS were 2.11 (95% CI 1.44-3.09) for HERC5, 0.14 (95% CI 0.05-0.41) for AIF1, 0.48 (95% CI 0.34-0.68) for CREB3L4, 0.54 (95% CI 0.42-0.68) for MLN, 0.23 (95% CI 0.13-0.38) for MRPL55, and 0.26 (95% CI 0.17-0.39) for SPAG11B. The hypothesis of a reverse causal relationship between these six circulating proteins and AS is not supported. Three of the six protein-coding genes were expressed in both the AS and healthy control groups, while CREB3L4, MLN, and SPAG11B were not detected. Increased levels of HERC5 predicted by genes are related to increased AS risk, whereas the levels of the remaining five circulating proteins, AIF1, CREB3L4, MLN, MRPL55, and SPAG11B, negatively correlate with AS risk. HERC5, AIF1, and MRPL55 are potential therapeutic targets for AS. This study advanced the field by employing a novel combination of proteomic range two-sample MR analysis and single-cell expression analysis to identify potential protein markers and therapeutic targets for AS. This approach enabled a comprehensive understanding of the causal relationships between circulating proteins and AS, which has not been extensively explored in previous studies.

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes.

Most phenotype-associated genetic variants map to non-coding regulatory regions of the human genome. Moreover, variants associated with blood cell phenotypes are enriched in regulatory regions active during hematopoiesis. To systematically explore the nature of these regions, we developed a highly efficient strategy, Perturb-multiome, that makes it possible to simultaneously profile both chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of a range of master transcription factors (TFs). This approach allowed us to examine the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within the TF-sensitive accessible chromatin regions, while representing less than 0.3% of the genome, show a ~100-fold enrichment in heritability across certain blood cell phenotypes; this enrichment is strikingly higher than for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

Ultrasound pulse repetition frequency preferentially activates different neuron populations independent of cell type

Objective. Transcranial ultrasound (US) stimulation serves as an external input to a neuron, and thus the evoked response relies on neurons' intrinsic properties. Neural activity is limited to a couple hundred hertz and often exhibits preference to input frequencies. Accordingly, US pulsed at specific physiologic pulse repetition frequencies (PRFs) may selectively engage neurons with the corresponding input frequency preference. However, most US parametric studies examine the effects of supraphysiologic PRFs. It remains unclear whether pulsing US at different physiologic PRFs could activate distinct neurons in the awake mammalian brain.Approach. We recorded cellular calcium responses of individual motor cortex neurons to US pulsed at PRFs of 10, 40, and 140 Hz in awake mice. We compared the evoked responses across these PRFs in the same neurons. To further understand the cell-type dependent effects, we categorized the recorded neurons as parvalbumin positive fast spiking interneurons or putative excitatory neurons and analyzed single-cell mechanosensitive channel expression in mice and humans using the Allen Brain Institute's RNA-sequencing databases.Main results. We discovered that many neurons were preferentially activated by only one PRF and different PRFs selectively engaged distinct neuronal populations. US-evoked cellular calcium responses exhibited the same characteristics as those naturally occurring during spiking, suggesting that US increases intrinsic neuronal activity. Furthermore, evoked responses were similar between fast-spiking inhibitory neurons and putative excitatory neurons. Thus, variation in individual neuron's cellular properties dominates US-evoked response heterogeneity, consistent with our observed cell-type independent expression patterns of mechanosensitive channels across individual neurons in mice and humans. Finally, US transiently increased network synchrony without producing prolonged over-synchronization that could be detrimental to neural circuit functions.Significance. These results highlight the feasibility of activating distinct neuronal subgroups by varying PRF and the potential to improve neuromodulation effects by combining physiologic PRFs.

SEPT9: From pan-cancer to lung squamous cell carcinoma

BackgroundSEPT9 is a pivotal cytoskeletal GTPase that regulates diverse biological processes encompassing mitosis and cytokinesis. While previous studies have implicated SEPT9 in tumorigenesis and development; comprehensive pan-cancer analyses have not been performed. This study aims to systematically explore its role in cancer screening, prognosis, and treatment, addressing this critical gap.MethodsGene and protein expression data containing clinical information were obtained from public databases for pan-cancer analyses. Additionally, clinical samples from 90 patients with lung squamous cell carcinoma (LUSC) were used to further experimentally validate the clinical significance of SEPT9. In addition, the molecular docking tool was used to analyze the affinities between SEPT9 protein and drugs.ResultsSEPT9 is highly expressed in various cancers, and its aberrant expression correlates with genetic alternations and epigenetic modifications, leading to adverse clinical outcomes. Take LUSC as an example, additional dataset analyses and immunohistochemical experiments further confirm the diagnostic and prognostic values as well as the clinical relevance of the SEPT9 gene and protein. Functional enrichment, single-cell expression, and immune infiltration analyses revealed that SEPT9 promotes malignant tumor progression and modulates the immune microenvironments, enabling patients to benefit from immunotherapy. Moreover, drug sensitivity and molecular docking analyses showed that SEPT9 is associated with the sensitivity and resistance of multiple drugs and has stable binding activity with them, including Vorinostat and OTS-964. To harness its prognostic and therapeutic potential in LUSC, a mitotic spindle-associated prognostic model including SEPT9, HSF1, ARAP3, KIF20B, FAM83D, TUBB8, and several clinical characteristics, was developed. This model not only improves clinical outcome predictions but also reshapes the immune microenvironment, making immunotherapy more effective for LUSC patients.ConclusionThis is the first study to systematically analyze the role of SEPT9 in cancers and innovatively apply the mitotic spindle-associated model to LUSC, fully demonstrating its potential as a valuable biomarker for cancer screening and prognosis, and highlighting its application value in promoting immunotherapy and chemotherapy, particularly for LUSC.

PMEL is involved in snake colour pattern transition from blotches to stripes

Corn snakes are emerging models for animal colouration studies. Here, we focus on the Terrazzo morph, whose skin pattern is characterized by stripes rather than blotches. Using genome mapping, we discover a disruptive mutation in the coding region of the Premelanosome protein (PMEL) gene. Our transcriptomic analyses reveal that PMEL expression is significantly downregulated in Terrazzo embryonic tissues. We produce corn snake PMEL knockouts, which present a comparable colouration phenotype to Terrazzo and the subcellular structure of their melanosomes and xanthosomes is also similarly impacted. Our single-cell expression analyses of wild-type embryonic dorsal skin demonstrate that all chromatophore progenitors express PMEL at varying levels. Finally, we show that in wild-type embryos PMEL-expressing cells are initially uniformly spread before forming aggregates and eventually blotches, as seen in the adults. In Terrazzo embryos, the aggregates fail to form. Our results provide insights into the mechanisms governing colouration patterning in reptiles.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

Investigation and Confirmation of PYCARD as a Potential Biomarker for the Management of Psoriasis Disease.

Psoriasis is not yet completely curable, and its etiology and pathogenesis are unclear. Necroptosis, also known as programmed necrosis, is a regulated mode of necrotic cell death. The interaction between inflammatory diseases and necrotic apoptosis has recently attracted significant attention. We explored the molecular mechanisms of necrotic apoptosis-related genes in psoriasis using bioinformatics methods to identify potential biomarkers for psoriasis. In this study, we screened psoriasis differentially expressed genes from the datasets GSE13355 and GSE14905 and took intersections with necrotic apoptosis-related genes for the next analysis. We used multiple machine learning algorithms to screen key genes and perform enrichment analysis. In addition, we performed an immune infiltration analysis. Transcription factors were predicted by the R package "RcisTarget". We also observed the cellular clustering of key genes in different cell types at the single-cell sequencing level. We used real-time fluorescence-based quantitative-polymerase chain reaction, Western blot, and immunohistochemistry to analyze gene expression in clinical samples. We constructed an imiquimod-induced psoriasis-like dermatitis model in mice for further validation. Seven key genes were screened as follows: AIM2, CARD6, HPSE, MYD88, PYCARD, RAI14, and TNFSF10. Enrichment analysis showed that the key genes were mainly involved in inflammatory pathways. Immune infiltration analysis showed significantly higher levels of CD8 T cells, CD4 initial T cells, and CD4 memory-activated T cells in the disease group's samples than in the normal patients' samples. The key gene expression in single cells analyzed showed that PYCARD was significantly expressed in keratinocytes. PYCARD was selected for gene expression analysis; the results showed that its expression was significantly elevated in the skin lesion tissues of patients with psoriasis. We also verified that PYCARD might play a vital role in the development of psoriasis skin lesions using animal experiments. PYCARD plays a vital role in psoriasis development and is a potential biomarker for psoriasis.

439. INTEGRATION OF SCRNA-SEQ AND BULK RNA-SEQ TO VALIDATE A PROGNOSTIC ASSOCIATED SIGNATURE IN ESOPHAGEAL SQUAMOUS CELL CARCINOMA

Abstract Background Considerable evidence has identified important targets for esophageal squamous cell carcinoma (ESCC), but its efficacy and the heterogeneity of ESCC are the main causes of treatment failure. Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity at the single-cell level and helps to better identify potential therapeutic targets. Methods ESCC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database. Single-sample gene set enrichment analysis (ssGSEA) was performed using the limma package to analyze ESCC-associated prognostic marker genes from ESCC transcriptome data downloaded from the TCGA database and the results were validated with transcriptomic data from 56 ESCC patients in our hospital. KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of prognostic marker genes were performed by GSEA. Then, univariate and multivariate Cox and lasso regressions were performed to further build signatures. Finally, qPCR was used to assess the expression of prognostic marker genes in 5 pairs ESCC samples. Results Integrate scRNA-seq (GSE53624) datasets to obtain 13 cell clusters. We characterized single-cell expression profiles of 24076 cell samples and identified 4 marker genes associated with prognosis. Four key prognostic marker genes were trained and validated on the TCGA-ESCA dataset and in 55 patients from our in-hospital cohort to evaluate predictive overall survival (OS) models. Correlation analysis showed that prognostic marker genes were independent of risk clinical characteristics. In addition, we performed Cox regression analysis, suggesting that the 4 prognostic marker genes screened were independent risk factors related to the prognosis of ESCC patients. In addition, qPCR showed that the expression of IER3 and SAA1 was decreased and the expression of CDKN3 was increased in ESCC tissues, while the expression of TINP3 was not significantly different. Conclusion We constructed prognostic marker genes by scRNA-seq and validated the model in a large patient population as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

Recovering single-cell expression profiles from spatial transcriptomics with scResolve.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained throughscResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

ScCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.

Genetic analysis from multiple cohorts implies causality between 2200 druggable genes, telomere length, and leukemia

BackgroundClinical therapeutic targets for leukemia remain to be identified and the causality between leukemia and telomere length is unclear. MethodsThis work employed cis expression quantitative trait locus (eQTL) for 2,200 druggable genes from the eQTLGen Consortium and genome-wide association studies (GWAS) summary data for telomere length in seven blood cell types from the UK Biobank, Netherlands Cohort as exposures. GWAS data for lymphoid leukemia (LL) and myeloid leukemia (ML) from FinnGen and Lee Lab were used as outcomes for discovery and replication cohorts, respectively. Robust Mendelian randomization (MR) findings were generated from seven MR models and a series of sensitivity analyses. Summary-data-based MR (SMR) analysis and transcriptome-wide association studies (TWAS) were further implemented to verify the association between identified druggable genes and leukemia. Single-cell type expression analysis was employed to identify the specific expression of leukemia casual genes on human bone marrow and peripheral blood immune cells. Multivariable MR analysis, linkage disequilibrium score regression (LDSC), and Bayesian colocalization analysis were performed to further validate the relationship between telomere length and leukemia. Mediation analysis was used to assess the effects of identified druggable genes affecting leukemia via telomere length. Phenome-wide MR (Phe-MR) analysis for assessing the effect of leukemia causal genes and telomere length on 1,403 disease phenotypes. ResultsCombining the results of the meta-analysis for MR estimates from two cohorts, SMR and TWAS analysis, we identified five LL causal genes (TYMP, DSTYK, PPIF, GDF15, FAM20A) and three ML causal genes (LY75, ADA, ABCA2) as promising drug targets for leukemia. Univariable MR analysis showed genetically predicted higher leukocyte telomere length increased the risk of LL (odds ratio [OR] = 2.33, 95 % confidence interval [95 % CI] 1.70–3.18; P = 1.33E-07), and there was no heterogeneity and horizontal pleiotropy. Evidence from the meta-analysis of two cohorts strengthened this finding (OR = 1.88, 95 % CI 1.06–3.05; P = 0.01). Multivariable MR analysis showed the causality between leukocyte telomere length and LL without interference from the other six blood cell telomere length (OR = 2.72, 95 % CI 1.88–3.93; P = 1.23E-07). Evidence from LDSC supported the positive genetic correlation between leukocyte telomere length and LL (rg = 0.309, P = 0.0001). Colocalization analysis revealed that the causality from leukocyte telomere length on LL was driven by the genetic variant rs770526 in the TERT region. The mediation analysis via two-step MR showed that the causal effect from TYMP on LL was partly mediated by leukocyte telomere length, with a mediated proportion of 12 %. ConclusionOur study identified several druggable genes associated with leukemia risk and provided new insights into the etiology and drug development of leukemia. We also found that genetically predicted higher leukocyte telomere length increased LL risk and its potential mechanism of action.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver

Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite’s unique division by internal budding, short cell cycle, and jumbled up classically defined cell cycle stages have restrained in-depth transcriptional program analysis. Here, unbiased transcriptome and chromatin accessibility maps throughout the lytic cell cycle are established at the single-cell level. Correlated pseudo-timeline assemblies of expression and chromatin profiles maps transcriptional versus chromatin level transition points promoting the cell division cycle. Sequential clustering analysis identifies functionally related gene groups promoting cell cycle progression. Promoter DNA motif mapping reveals patterns of combinatorial regulation. Pseudo-time trajectory analysis reveals transcriptional bursts at different cell cycle points. The dominant burst in G1 is driven largely by transcription factor AP2XII-8, which engages a conserved DNA motif, and promotes the expression of 44 ribosomal proteins encoding regulon. Overall, the study provides integrated, multi-level insights into apicomplexan transcriptional regulation.

Single Cell Expression Systems for the Production of Recombinant Proteins for Immunodiagnosis and Immunoprophylaxis of Toxoplasmosis.

Toxoplasmosis represents a significant public health and veterinary concern due to its widespread distribution, zoonotic transmission, and potential for severe health impacts in susceptible individuals and animal populations. The ability to design and produce recombinant proteins with precise antigenic properties is fundamental, as they serve as tools for accurate disease detection and effective immunization strategies, contributing to improved healthcare outcomes and disease control. Most commonly, a prokaryotic expression system is employed for the production of both single antigens and multi-epitope chimeric proteins; however, the cloning strategies, bacterial strain, vector, and expression conditions vary. Moreover, literature reports show the use of alternative microbial systems such as yeast or Leishmania tarentolae. This review provides an overview of the methods and strategies employed for the production of recombinant Toxoplasma gondii antigenic proteins for the serological detection of T. gondii infection and vaccine development.

Macular structural integrity estimates are associated with Parkinson’s disease genetic risk

BackgroundOptical coherence tomography (OCT) is a non-invasive technique to measure retinal layer thickness, providing insights into retinal ganglion cell integrity. Studies have shown reduced retinal nerve fibre layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness in Parkinson’s disease (PD) patients. However, it is unclear if there is a common genetic overlap between the macula and peripapillary estimates with PD and if the genetic risk of PD is associated with changes in ganglion cell integrity estimates in young adults.MethodWestern Australian young adults underwent OCT imaging. Their pRNFL, GCIPL, and overall retinal thicknesses were recorded, as well as their longitudinal changes between ages 20 and 28. Polygenic risk scores (PRS) were estimated for each participant based on genome-wide summary data from the largest PD genome-wide association study conducted to date. We further evaluated whether PD PRS was associated with changes in thickness at a younger age. To evaluate the overlap between retinal integrity estimates and PD, we annotated and prioritised genes using mBAT-combo and performed colocalisation through the GWAS pairwise method and HyPrColoc. We used a multi-omic approach and single-cell expression data of the retina and brain through a Mendelian randomisation framework to evaluate the most likely causal genes. Genes prioritised were analysed for missense variants that could have a pathogenic effect using AlphaMissense.ResultsWe found a significant association between the Parkinson’s disease polygenic risk score (PD PRS) and changes in retinal thickness in the macula of young adults assessed at 20 and 28 years of age. Gene-based analysis identified 27 genes common to PD and retinal integrity, with a notable region on chromosome 17. Expression analyses highlighted NSF, CRHR1, and KANSL1 as potential causal genes shared between PD and ganglion cell integrity measures. CRHR1 showed consistent results across multiple omics levels.InterpretationOur findings suggest that retinal measurements, particularly in young adults, could be a potential marker for PD risk, indicating a genetic overlap between retinal structural integrity and PD. The study highlights specific genes and loci, mainly on chromosome 17, as potential shared etiological factors for PD and retinal changes. Our results highlight the importance of further longitudinal studies to validate retinal structural metrics as early indicators of PD predisposition.

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.

The neocortical infrastructure for language involves region-specific patterns of laminar gene expression

The language network of the human brain has core components in the inferior frontal cortex and superior/middle temporal cortex, with left-hemisphere dominance in most people. Functional specialization and interconnectivity of these neocortical regions is likely to be reflected in their molecular and cellular profiles. Excitatory connections between cortical regions arise and innervate according to layer-specific patterns. Here, we generated a gene expression dataset from human postmortem cortical tissue samples from core language network regions, using spatial transcriptomics to discriminate gene expression across cortical layers. Integration of these data with existing single-cell expression data identified 56 genes that showed differences in laminar expression profiles between the frontal and temporal language cortex together with upregulation in layer II/III and/or layer V/VI excitatory neurons. Based on data from large-scale genome-wide screening in the population, DNA variants within these 56 genes showed set-level associations with interindividual variation in structural connectivity between the left-hemisphere frontal and temporal language cortex, and with the brain-related disorders dyslexia and schizophrenia which often involve affected language. These findings identify region-specific patterns of laminar gene expression as a feature of the brain's language network.

Rational combinatorial targeting by adapter CAR-T-cells (AdCAR-T) prevents antigen escape in acute myeloid leukemia

Targeting AML by chimeric antigen receptor T-cells (CAR-T) is challenging due to the promiscuous expression of AML-associated antigens in healthy hematopoiesis and high degree of inter- and intratumoral heterogeneity. Here, we present single-cell expression data of AML-associated antigens in 30 primary pediatric AML samples. We identified CD33, CD38, CD371, IL1RAP and CD123 as the most frequently expressed. Notably, high variability was observed not only across the different patient samples but also among leukemic cells of the same patient suggesting the necessity of multiplexed targeting approaches. To address this need, we utilized our modular Adapter CAR (AdCAR) platform, enabling precise qualitative and quantitative control over CAR-T-cell function. We show highly efficient and target-specific activity for newly generated adapter molecules (AMs) against CD33, CD38, CD123, CD135 and CD371, both in vitro and in vivo. We reveal that inherent intratumoral heterogeneity in antigen expression translates into antigen escape and therapy failure to monotargeted CAR-T therapy. Further, we demonstrate in PDX models that rational combinatorial targeting by AdCAR-T-cells can cure heterogenic disease. In conclusion, we elucidate the clinical relevance of heterogeneity in antigen expression in pediatric AML and present a novel concept for precision immunotherapy by combinatorial targeting utilizing the AdCAR platform.

ZFC3H1 as an Indicator of Poor Prognosis in Hepatocellular Carcinoma: Bioinformatic Analysis and Experimental Verification.

Zinc finger C3H1-type containing (ZFC3H1) might regulate RNA processes. However, research lacks the prognostic value of ZFC3H1 in hepatocellular carcinoma (HCC). The study analyzed ZFC3H1 expression in HCC cells and its correlation with patient prognosis using transcriptomics, immunohistochemistry, and quantitative real-time reverse transcription PCR, as well as single-cell RNA expression data. Additionally, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were used to investigate the potential ZFC3H1-related cellular functions and signaling pathways. The impact of ZFC3H1 expression on the tumor microenvironment and tumor mutational burden (TMB) was assessed using the ESTIMATE algorithm. Cell-based assays, including cell counting kit 8, proliferation, colony formation, cell cycle, wound healing, and Transwell assays, were conducted to evaluate the influence of ZFC3H1 on hepatocellular carcinoma proliferation and migration. ZFC3H1 is upregulated in HCC and linked to tumor progression. High ZFC3H1 expression is a prognostic risk factor for HCC, according to Kaplan-Meier and Cox regression analyses. ESTIMATE analysis suggested that ZFC3H1 reduces immune cell infiltration and increases the TMB. Patients with low ZFC3H1 expression might respond better to immunotherapy. High ZFC3H1 expression is associated with increased half-maximal inhibitory concentration (IC50) of sorafenib. Functional experiments demonstrated that reducing ZFC3H1 expression inhibited HCC cell proliferation and migration. ZFC3H1 is upregulated in HCC, promoting the proliferation and migration of liver cancer cells, impacting the prognosis of HCC patients and the effectiveness of immunotherapy. ZFC3H1 might serve as a therapeutic target and biomarker for HCC.