Single-cell Level Research Articles

ScDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses.

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

Abstract C117: Integrative multi-omics profiling in patients of African descent diagnosed with prostate cancer reveals distinct tumor-promoting immunosuppressive niches at a single-cell level and spatial resolution

Abstract In the United States and globally, prostate cancer (PCa) mortality rates are the highest among men of African descent. 24% of the disparities in PCa remains even when controlled for access to care and stage at presentation. Moreover, the tumor microenvironment plays an essential role in tumor progression, aggressiveness, therapeutic response, and patient outcomes. Additionally, the association of the genomic findings with patient ancestry and other characteristics, such as tumor biology and transcriptomic alterations, remains poorly understood. Here, we performed a multi-Omics approach (N=447) to unravel the complexity of tumor heterogeneity and understand disease progression & distinct tumor biology influenced by genetic ancestry. We performed Whole Exome (Normal/tumor paired) Sequencing matched with Methyl Seq and Whole transcriptomic Sequencing for three datasets of our African, African American Men (AAM), and European Men (EAM). Additionally, we ran Spatial NanoString high-plex GeoMx-DSP for a total of 118 treatment-naive PCa patients (around 500 ROIs). Simultaneously, we added matched whole transcriptome Sequencing for these patients. The cohort comprised 87 AAM, 3 unknown, and 28 EAM self-reported individuals. To verify the self-reported race, the genomic ancestry was qualified using genotype and Admixture analysis. To further validate differentially expressed genes at the protein level, we performed multi-plex histological staining of 40 markers to determine the spatial resolution and neighborhood clustering within the tumor and the microenvironment. In parallel, we performed scMultiOmics sequencing (scRNA & scATAC-Seq) at a single-cell resolution (6000 cells/sample at 25K reads per cell) from an additional 12 patients (9 AAM & 3 EAM). We reconstructed a 3D model of the distinct tumor microenvironment at subcellular resolution using serially sectioned hematoxylin and eosin- stained tissue sections (CODA technology). Our results demonstrate that patients who self-report as AAM or Nigerian are assigned to high African (> 70%) Ancestry with either Yoruba (Nigeria) and/or Bantu subpopulation in the Sub-Saharan area. Additionally, high African Ancestry patients are diagnosed at a younger age and show advanced pathology stages compared to patients with European Ancestry. AAM of Yoruba descent expresses significantly higher immune-inflammatory signatures (STAT/IFNG-signaling pathway) compared to the Nigerian-Yoruba subpopulation. Our scRNA-Seq analysis shows that AAM has suppressive myeloid cells infiltrate within the tumor cells. The infiltrations of these cells change with age, Gleason Grade, and pathology stage. Moreover, AAM-tumor cells (scATAC-Seq) have increased open Chromatin accessibility with STAT motifs enrichment (p-value;0.0001), which could be the driver regulators of the immune-suppressive signature as well as influence the upregulation of STAT/IFNG signature within African Ancestry patients. Our study provides new insight into how genetic ancestry impacts immune signatures in AAM/African and contributes to PCa racial disparities. Citation Format: Isra Elhussin, Ezra Baraban1, Ashley Kiemen, Tamara L Lotan, Cathy Handy Marshall, Emmanuel Antonarakis, Moray J Campbell, Melissa Davis, Michael Dixon, Isaac Kim, Stefan Ambs, Rick Kittles, Adam B Murphy, Clayton Yates. Integrative multi-omics profiling in patients of African descent diagnosed with prostate cancer reveals distinct tumor-promoting immunosuppressive niches at a single-cell level and spatial resolution [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C117.

Multi-marker analysis of circulating tumor cells in localized intermediate/high-risk and metastatic prostate cancer

AbstractCirculating tumor cells (CTCs) are an established prognostic marker in metastatic prostate cancer (PrC) but have received little attention in localized high-risk disease. Peripheral blood was obtained from patients with early intermediate and high-risk PrC (n = 15) at baseline, after radiotherapy, and during follow-up, as well as from metastatic PrC patients (n = 23). CTCs were enriched using the microfluidic Parsortix® technology. CTC-related marker were quantified with qPCR and RNA in-situ hybridization (ISH). Positivity and associations to clinical parameters were assessed using McNemar test, Fisher Exact test or log-rank test. The overall positivity was high in both cohorts (87.0% metastatic vs. 66.7% early at baseline). A high concordance of qPCR and RNA ISH was achieved. In metastatic PrC, PSA and PSMA were prognostic for shorter overall survival. In early PrC patients, an increase of positive transcripts per blood sample was observed from before to after radiation therapy, while a decrease of positive markers was observed during follow-up. CTC analysis using the investigated qPCR marker panel serves as tool for achieving high detection rates of PrC patient samples even in localized disease. RNA ISH offers the advantage of confirming these markers at the single cell level. Employing the clinically relevant marker PSMA, our CTC approach can be used for diagnostic purposes to screen patients profiting from PSMA-directed PET-CT or PSMA-targeted therapy.

Abstract A061: The impact of race and obesity on the prostate tumor microenvironment

Abstract Black Americans have the highest prostate cancer incidence and are twice as likely to die from their disease compared to Non-Hispanic White men, making this one of the largest cancer disparities between these two racial groups in the United States. While access to health care is one of the strongest contributors to this disparity, studies suggest biological distinctions exist between tumors from Black and Non-Hispanic White men. Furthermore, obesity is associated with worse prostate cancer outcomes, and research showing a stronger association between obesity and prostate cancer risk among Black men compared to Non-Hispanic White men suggests obesity and Black race may work in combination to create aggressive disease. Hence, we sought to thoroughly and precisely characterize tumors from Black men, with and without obesity, to identify tumor behavior and potential therapeutic options for this population, which to date, has not been done mostly because of the lack of racially diverse cohorts of prostate cancer. Also, we sought to determine the impact of obesity on the composition of the prostate tumor microenvironment, which to date remains unknown. Given that in the US obesity rates are over 40% and the pressing need to better address high-risk populations, it is critical to better understand how obesity and race impact disease biology. We hypothesized obesity and Black race, individually and combined, increase tumor immune infiltration, creating a distinct tumor microenvironment. Specifically, we hypothesized this microenvironment will be characterized by higher overall inflammation, but also will enable us to identify therapeutic targets that in time can lead to the creation of personalized treatments for this population. To test this hypothesis, we used a large and racially diverse patient cohort from men who underwent radical prostatectomy at the Veterans Affairs Medical Center in Durham, North Carolina. To analyze the tumor microenvironment at the single cell level and define spatial organization, we used imaging mass cytometry in tissue microarrays of tumor and normal prostate samples from patients similar in tumor stage. We created a customized marker panel to determine cell phenotype and expression of immune checkpoints. We detected 25 cell phenotypes in the prostate microenvironment and determined differential phenotypic abundance and interactions by race and obesity. Next, we will expand our study to include a larger number of samples and associate tumor microenvironment characteristics with patient recurrence, metastasis, and survival. In this study, we generated new insights into disease drivers in an understudied and disproportionately affected population and lay important groundwork for future research identifying novel therapeutic targets for high-risk populations, such as men with African descent and obesity. Citation Format: Gloria Cecilia Galvan, Simeon Mahov, Sungyong You, Simon Knott, Michael Freeman, Akil Merchant, Stephen J Freedland. The impact of race and obesity on the prostate tumor microenvironment [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A061.

Miniaturized protein profiling permits targeted signaling pathway analysis in individual circulating tumor cells to improve personalized treatment

BackgroundTraditional genomic profiling and mutation analysis of single cells like Circulating Tumor Cells (CTCs) fails to capture post-translational and functional alterations of proteins, often leading to limited treatment efficacy. To overcome this gap, we developed a miniaturized ‘protein analysis on the single cell level’ workflow—baptized ZeptoCTC. It integrates established technologies for single-cell isolation with sensitive Reverse Phase Protein Array (RPPA) analysis, thus enabling the comprehensive assessment of multiple protein expression and activation in individual CTCs.MethodsThe ZeptoCTC workflow involves several critical steps. Firstly, individual cells are labeled and isolated. This is followed by cell lysis and the printing of true single cell lysate preparations onto a ZeptoChip using a modified micromanipulator, CellCelector™. The printed lysates then undergo fluorescence immunoassay RPPA protein detection using a ZeptoReader. Finally, signal quantification is carried out with Image J software, ensuring precise measurement of multiple protein levels.ResultsThe efficacy of ZeptoCTC was demonstrated through various applications. Initially, it was used for measuring EpCAM protein expression, a standard marker for CTC detection, revealing higher levels in single MCF-7 over MDA-MB-231 tumor cells. Furthermore, in Capivasertib (Akt-inhibitor)-treated MCF-7 single cells, ZeptoCTC detected a 2-fold increase in the pAkt/Akt ratio compared to control cells, and confirmed co-performed bulk-cell western blot analysis results. Notably, when applied to individual CTCs from metastasized breast cancer patients, ZeptoCTC revealed significant differences in protein activation levels, particularly in measured pAkt and pErk levels, compared to patient-matched WBCs. Moreover, it successfully differentiated between CTCs from patients with different Akt1 genotypes, highlighting its potential to determine the activation status of druggable cancer driving proteins for individual and targeted treatment decision making.ConclusionsThe ZeptoCTC workflow represents a valuable tool in single cell cancer research, crucial for personalized medicine. It permits detailed analysis of key proteins and their activation status of targeted, cancer-driven signaling pathways in single cell samples, aiding in understanding tumor response, progression, and treatment efficacy beyond bulk analysis. The method significantly advances clinical investigations in cancer, improving treatment precision and effectiveness. The workflow will be applicable to protein analysis on other types of single cells like relevant in stem cell, neuropathology and hemopoietic cell research.

Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries.

Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared with existing single-cell methodologies, raising questions regarding the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.

Unraveling developmental patterns and differentiation trajectories in a single developing internode of Moso Bamboo (Phyllostachys edulis)

Bamboo, a significant carbon sink and eco-friendly resource, possesses substantial economic value and ecological benefits. A single internode represents the fundamental unit of bamboo culm, yet research focusing on the pivotal cellular destinies and molecular mechanisms underpinning the growth and development of a single internode remains comparatively scarce. In the study, we pioneered the establishment of a high-resolution single-cell transcriptomic atlas for a single internode of Moso bamboo. Utilizing known marker genes, we identified six major cell clusters (meristem, epidermis, phloem, xylem, pre-xylem/phloem, and vascular bundle sheath) and found many potential marker genes in the internode of Moso bamboo. Subsequently, we reconstructed the developmental trajectories of the epidermis and vascular tissues and further unveiled the key regulatory factors determining cell fates. Moreover, ectopic expression of PheIAA15 in Arabidopsis thaliana resulted in a marked reduction in the number of vascular bundles within the stems, as well as a significant decrease in their cross-sectional areas, diverging from existing research in Arabidopsis. The interaction between PheIAA15 and PheWOX4c suggested a potential novel auxin response mechanism in Moso bamboo. We subsequently elucidated that PheIAA15 bound to the promoter region of PheCLE25, thereby down-regulating the expression of PheCLE25, whereas PheWOX4c can activate the transcription of PheCLE25, which suggested that these two factors regulated the expression of PheCLE25 through an antagonistic mechanism. This finding highlighted the critical role of the complex regulatory network established among these elements in the differentiation of vascular tissues in the internodes of Moso bamboo. Overall, the study fills a critical research void concerning internodal development in bamboo plants at the single-cell level, laying a foundation for future studies to unravel the origin and differentiation of vascular bundle cells during internode development in perennial Gramineous plants.

Modular, inducible, and titratable expression systems for Escherichia coli and Acinetobacter baumannii.

Gene expression systems that transcend species barriers are needed for cross-species analysis of gene function. In particular, expression systems that can be utilized in both model and pathogenic bacteria underpin comparative functional approaches that inform conserved and variable features of bacterial physiology. In this study, we develop replicative and integrative vectors alongside a novel, IPTG-inducible promoter that can be used in the model bacterium Escherichia coli K-12 as well as strains of the antibiotic-resistant pathogen, Acinetobacter baumannii. We generate modular vectors that transfer by conjugation at high efficiency and either replicate or integrate into the genome, depending on design. Embedded in these vectors, we also developed a synthetic, IPTG-inducible promoter, PabstBR, that induces to a high level but is less leaky than the commonly used trc promoter. We show that PabstBR is titratable at both the population and single-cell levels, regardless of species, highlighting the utility of our expression systems for cross-species functional studies. Finally, as a proof of principle, we use our integrating vector to develop a reporter for the E. coli envelope stress σ factor, RpoE, and deploy the reporter in E. coli and A. baumannii, finding that A. baumannii does not recognize RpoE-dependent promoters unless RpoE is heterologously expressed. We envision that these vector and promoter tools will be valuable for the community of researchers who study the fundamental biology of E. coli and A. baumannii.IMPORTANCEAcinetobacter baumannii is a multidrug-resistant, hospital-acquired pathogen with the ability to cause severe infections. Understanding the unique biology of this non-model bacterium may lead to the discovery of new weaknesses that can be targeted to treat antibiotic-resistant infections. In this study, we provide expression tools that can be used to study the gene function in A. baumannii, including in drug-resistant clinical isolates. These tools are also compatible with the model bacterium, Escherichia coli, enabling cross-species comparisons of gene function. We anticipate that the use of these tools by the scientific community will accelerate our understanding of Acinetobacter biology.

ImSpiRE: image feature-aided spatial resolution enhancement method.

The resolution of most spatially resolved transcriptomic technologies usually cannot attain the single-cell level, limiting their applications in biological discoveries. Here, we introduce ImSpiRE, an image feature-aided spatial resolution enhancement method for in situ capturing spatial transcriptome. Taking the information stored in histological images, ImSpiRE solves an optimal transport problem to redistribute the expression profiles of spots to construct new transcriptional profiles with enhanced resolution, together with extending the gene expression profiles into unmeasured regions. Applications to multiple datasets confirm that ImSpiRE can enhance spatial resolution to the subspot level while contributing to the discovery of tissue domains, signaling communication patterns, and spatiotemporal characterization.

Treatment resistance to melanoma therapeutics on a single cell level

Therapy targeting the BRAF-MEK cascade created a treatment revolution for patients with BRAF mutant advanced melanoma. Unfortunately, 80% patients treated will progress by 5 years follow-up. Thus, it is imperative we study mechanisms of melanoma progression and therapeutic resistance. We created a scRNA (single cell RNA) atlas of 128,230 cells from 18 tumors across the treatment spectrum, discovering melanoma cells clustered strongly by transcriptome profiles of patients of origins. Our cell-level investigation revealed gains of 1q and 7q as likely early clonal events in metastatic melanomas. By comparing patient tumors and their derivative cell lines, we observed that PD1 responsive tumor fraction disappears when cells are propagated in vitro. We further established three anti-BRAF-MEK treatment resistant cell lines using three BRAF mutant tumors. ALDOA and PGK1 were found to be highly expressed in treatment resistant cell populations and metformin was effective in targeting the resistant cells. Our study suggests that the investigation of patient tumors and their derivative lines is essential for understanding disease progression, treatment response and resistance.

A Generalized Adder mechanism for Cell Size Homeostasis: Implications for Stochastic Dynamics of Clonal Proliferation.

Measurements of cell size dynamics have revealed phenomeno-logical principles by which individual cells control their size across diverse organisms. One of the emerging paradigms of cell size homeostasis is the adder , where the cell cycle duration is established such that the cell size increase from birth to division is independent of the newborn cell size. We provide a mechanistic formulation of the adder considering that cell size follows any arbitrary non-exponential growth law . Our results show that the main requirement to obtain an adder regardless of the growth law (the time derivative of cell size) is that cell cycle regulators are produced at a rate proportional to the growth law and cell division is triggered when these molecules reach a prescribed threshold level. Among the implications of this generalized adder, we investigate fluctuations in the proliferation of single-cell derived colonies. Considering exponential cell size growth, random fluctuations in clonal size show a transient increase and then eventually decay to zero over time (i.e., clonal populations become asymptotically more similar). In contrast, several forms of non-exponential cell size dynamics (with adder-based cell size control) yield qualitatively different results: clonal size fluctuations monotonically increase over time reaching a non-zero value. These results characterize the interplay between cell size homeostasis at the single-cell level and clonal proliferation at the population level, explaining the broad fluctuations in clonal sizes seen in barcoded human cell lines.

Efficient Strategy to Discover DNA Aptamers Against Low Abundance Cell Surface Proteins in Scarce Samples.

Molecular recognition probes targeting cell surface proteins such as aptamers play crucial roles in precise diagnostics and therapy. However, the selection of aptamers against low-abundance proteins in situ on the cell surface, especially in scarce samples, remains an unmet challenge. In this study, we present a single-round, single-cell aptamer selection method by employing a digital DNA sequencing strategy, termed DiDS selection, to address this dilemma. This approach incorporates a molecular identification card for each DNA template, thereby mitigating biases introduced by multiple PCR amplifications and ensuring the accurate identification of aptamer candidates. Through DiDS selection, we successfully obtained a series of high-quality aptamers against cell lines, clinical specimens, and neurons. Subsequent analyses for target identification revealed that aptamers derived from DiDS selection exhibit recognition capabilities for proteins with varying abundance levels. In contrast, multiple rounds of selection resulted in the enrichment of only one aptamer targeting a high-abundance target. Moreover, the comprehensive profiling of cell surfaces at the single-cell level, utilizing an enriched aptamer pool, revealed unique molecular patterns for each cell line. This streamlined approach holds promise for the rapid generation of specific recognition molecules targeting cell surface proteins across a broad range of expression levels and expands its applications in cell profiling, specific probe identification, biomarker discovery, etc.

Specialized Listeria monocytogenes produce tailocins to provide a population-level competitive growth advantage.

Tailocins are phage tail-like bacteriocins produced by various bacterial species to kill kin competitors. Given that tailocin release is dependent upon cell lysis, regulation of tailocin production at the single-cell and population level remains unclear. Here we used flow cytometry, competition assays and structural characterization of tailocin production in a human bacterial pathogen, Listeria monocytogenes. We revealed that a specialized subpopulation, constituting less than 1% of the total bacterial population, differentiates to produce, assemble and store thousands of tailocin particles. Tailocins are packed in a highly ordered manner, clustered in a liquid crystalline phase that occupies a substantial volume of the cell. Tailocin production confers a competitive growth advantage for the rest of the population. This study provides molecular insights into tailocin production as a form of altruism, showing how cell specialization within bacterial populations can confer competitive advantages at the population level.

Single Cell Sequencing and Spatial Transcriptomics in Kidney Transplantation

Abstract Purpose of Review Progress in biology and medicine relies on technological advances. The ability to examine gene expression from single cells in tissues with complex architecture is now possible with the advent of single cell sequencing and spatial transcriptomics methods. This review will outline how these methods have been applied to kidney transplantation and their inherent challenges and limitations. Recent Findings The transcriptomes of single cells can now be defined in health and disease allowing for the differences to be investigated. Advanced in situ sequencing and capture methodologies now add spatial resolution to single cell transcriptomics. However, such powerful methods bring challenges, most notably the associated computational burden that is required to make the resulting high-dimensional output data interpretable. A second challenge is that of cost. Thankfully, as with many new technologies’ costs will drop over time making these methods available to more research groups. Summary The sequencing and analysis of gene expression at the single cell level with spatial information retained is now feasible. These methods are major advances for biomedical research and bring huge potential with regards to the better understanding of disease at cellular and organ levels.

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BackgroundThe study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.MethodThree single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.ResultsThis study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup’s expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.DiscussionThis study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

Search and match across spatial omics samples at single-cell resolution.

Spatial omics technologies characterize tissue molecular properties with spatial information, but integrating and comparing spatial data across different technologies and modalities is challenging. A comparative analysis tool that can search, match and visualize both similarities and differences of molecular features in space across multiple samples is lacking. To address this, we introduce CAST (cross-sample alignment of spatial omics), a deep graph neural network-based method enabling spatial-to-spatial searching and matching at the single-cell level. CAST aligns tissues based on intrinsic similarities of spatial molecular features and reconstructs spatially resolved single-cell multi-omic profiles. CAST further allows spatially resolved differential analysis (∆Analysis) to pinpoint and visualize disease-associated molecular pathways and cell-cell interactions and single-cell relative translational efficiency profiling to reveal variations in translational control across cell types and regions. CAST serves as an integrative framework for seamless single-cell spatial data searching and matching across technologies, modalities and sample conditions.

N-acetylcysteine effects on extracellular polymeric substances of Xylella fastidiosa: A spatiotemporal investigation with implications for biofilm disruption

BackgroundThe matrix of extracellular polymeric substances (EPS) present in biofilms greatly amplifies the problem of bacterial infections, protecting bacteria against antimicrobial treatments and eventually leading to bacterial resistance. The need for alternative treatments that destroy the EPS matrix becomes evident. N-acetylcysteine (NAC) is one option that presents diverse effects against bacteria; however, the different mechanisms of action of NAC in biofilms have yet to be elucidated. ObjectivesIn this work, we performed microscopy studies at micro and nano scales to address the effects of NAC at single cell level and early-stage biofilms of the Xylella fastidiosa phytopathogen. MethodsWe show the physical effects of NAC on the adhesion surface and the different types of EPS, as well as the mechanical response of individual bacteria to NAC concentrations between 2 and 20 mg/mL. ResultsNAC modified the conditioning film on the substrate, broke down the soluble EPS, resulting in the release of adherent bacteria, decreased the volume of loosely bound EPS, and disrupted the biofilm matrix. Tightly bound EPS suffered structural alterations despite no solid evidence of its removal. In addition, bacterial force measurements upon NAC action performed with InP nanowire arrays showed an enhanced momentum transfer to the nanowires due to increased cell mobility resulting from EPS removal. ConclusionsOur results clearly show that conditioning film and soluble EPS play a key role in cell adhesion control and that NAC alters EPS structure, providing solid evidence that NAC actuates mainly on EPS removal, both at single cell and biofilm levels.

Analyses of single-cell and bulk RNA sequencing combined with machine learning reveal the expression patterns of disrupted mitophagy in schizophrenia.

Mitochondrial dysfunction is an important factor in the pathogenesis of schizophrenia. However, the relationship between mitophagy and schizophrenia remains to be elucidated. Single-cell RNA sequencing datasets of peripheral blood and brain organoids from SCZ patients and healthy controls were retrieved. Mitophagy-related genes that were differentially expressed between the two groups were screened. The diagnostic model based on key mitophagy genes was constructed using two machine learning methods, and the relationship between mitophagy and immune cells was analyzed. Single-cell RNA sequencing data of brain organoids was used to calculate the mitophagy score (Mitoscore). We found 7 key mitophagy genes to construct a diagnostic model. The mitophagy genes were related to the infiltration of neutrophils, activated dendritic cells, resting NK cells, regulatory T cells, resting memory T cells, and CD8 T cells. In addition, we identified 12 cell clusters based on the Mitoscore, and the most abundant neurons were further divided into three subgroups. Results at the single-cell level showed that Mitohigh_Neuron established a novel interaction with endothelial cells via SPP1 signaling pathway, suggesting their distinct roles in SCZ pathogenesis. We identified a mitophagy signature for schizophrenia that provides new insights into disease pathogenesis and new possibilities for its diagnosis and treatment.

The surface interface and swimming motility influence surface-sensing responses in Pseudomonas aeruginosa

Bacterial biofilms have been implicated in several chronic infections. After initial attachment, a critical first step in biofilm formation is a cell inducing a surface-sensing response. In the Gram-negative opportunistic pathogen Pseudomonas aeruginosa, two second messengers, cyclic diguanylate monophosphate (c-di-GMP) and cyclic adenosine monophosphate (cAMP), are produced by different surface-sensing mechanisms. However, given the disparate cellular behaviors regulated by these second messengers, how newly attached cells coordinate these pathways remains unclear. Some of the uncertainty relates to studies using different strains, experimental systems, and usually focusing on a single second messenger. In this study, we developed a tricolor reporter system to simultaneously gauge c-di-GMP and cAMP levels in single cells. Using PAO1, we show that c-di-GMP and cAMP are selectively activated in two commonly used experimental systems to study surface sensing. By further examining the conditions that differentiate a c-di-GMP or cAMP response, we demonstrate that an agarose-air interface activates cAMP signaling through type IV pili and the Pil-Chp system. However, a liquid-agarose interface favors the activation of c-di-GMP signaling. This response is dependent on flagellar motility and correlated with higher swimming speed. Collectively, this work indicates that c-di-GMP and cAMP signaling responses are dependent on the surface context.