Biochemical analysis of human brain tissue is typically done by homogenizing whole pieces of brain and separately characterizing the proteins, RNA, DNA, and other macromolecules within. While this has been sufficient to identify substantial changes, there is little ability to identify small changes or alterations that may occur in subsets of cells. To effectively investigate the biochemistry of disease in the brain, with its different cell types, we must first separate the cells and study them as phenotypically defined populations or even as individuals. In this project, we developed a new method for the generation of Whole Cell Dissociated Suspensions (WCDS) in fresh human brain tissue that could be shared as a resource with scientists to study single human cells or populations. Characterization of WCDS was done in paraffin-embedded sections stained with H&E, and by phenotyping with antibodies using immunohistochemistry and Fluorescence Activated Cell Sorting (FACS). Additionally, we compared extracted RNA from WCDS with RNA from adjacent intact cortical tissue, using RT-qPCR for cell-type-specific RNA for the same markers as well as whole transcriptome sequencing. More than 11,626 gene transcripts were successfully sequenced and classified using an external database either as being mainly expressed in neurons, astrocytes, microglia, oligodendrocytes, endothelial cells, or mixed (in two or more cell types). This demonstrates that we are currently capable of producing WCDS with a full representation of different brain cell types combined with RNA quality suitable for use in biochemical analysis.

BACKGROUND: Recent research reported that frailty was prevalent among adults with chronic kidney disease (CKD) in clinical trials, and monocytes illustrated a similar difference in these two diseases compared to the normal. However, the scientific evidence for a causal relationship between these two diseases was lacking, with further exploration into whether monocytes co-regulate them.
METHODS: We aimed to integrate large-scale Mendelian randomization (MR) and single-cell transcriptome analysis to determine whether there was a causal relationship between frailty and CKD (Bidirectional two-sample Mendelian determined the causal direction), whether monocytes impacted them, and whether the two diseases shared genetic variation sites. Based on 441 Genome-wide association study datasets, this study utilized five MR methods, multiple sensitivity analysis, and corresponding single-cell transcriptome datasets as proof.
RESULTS: The association between frailty and CKD was significantly causal, and frailty increased the risk of CKD in patients (OR (95 %CI): 3.5597 (1.8369-6.8982), p = 0.000168909). The exposure monocyte can increase the risk of frailty and CKD in patients, especially with high expression of HLA genes in these cells. The existing two-sample MR results cannot reject the hypothesis that monocytes increase the risk of CKD by inducing frailty. rs9275271\' 1mb genetic location above and below had been proven to be an effective genetic space for both frailty and CKD.
CONCLUSIONS: We conducted the largest MR to date on frailty, monocyte, and CKD, and found a significant causal association between frailty and CKD, with the single-cell analysis confirmed. The exposure monocytes increased the risk of frailty and CKD, particularly with high expression of HLA genes in these cells. We identified a potential common genetic variant space, rs9275271, associated with frailty and CKD, providing insights into the genetic basis of these conditions.

Chronic Lung Allograft Dysfunction (CLAD) is a critical post-transplant complication that predominantly determines the long-term survival rates and quality of life of patients undergoing lung transplantation. The limited efficacy of current immunosuppressive strategies underscores our incomplete understanding of the immunological aspects of CLAD. Hence, there is an urgent need for more comprehensive and targeted research to unravel the complex interplay of immune cells in the development and progression of CLAD. This study conducts an in-depth analysis of the immune environment in CLAD. By examining the gene expression profiles of T cells, natural killer cells, B cells, macrophages, and monocytes, we have elucidated a unique immunological landscape in CLAD compared to healthy controls. We highlight the heterogeneity within the immune populations and provide a comprehensive understanding of the immune mechanisms driving CLAD. Enrichment analysis identified specific pathways that are either overactive or suppressed in CLAD, revealing potential molecular targets for therapeutic intervention. Our findings emphasize the crucial role of T cells in the pathophysiology of CLAD, coordinating the immune response and revealing an amplified immune cell network, potentially leading to maladaptive tissue responses. By integrating a comprehensive cellular and molecular portrait of the immune environment, our research not only deepens our understanding of the pathogenesis of CLAD but also lays a foundational approach for the development of targeted therapies.

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Developments in single-cell technology have considerably changed the way we study biology. Significant efforts have been made over the last few years to build comprehensive cell-type-specific transcriptomic atlases for a wide range of tissues in several model organisms in order to discover cell-type-specific markers and drivers of gene expression. One such tissue is the ovary of the fruit-fly Drosophila melanogaster, which is a popular model system with wide-ranging applications in the study of both development and disease. Three independent studies have recently produced comprehensive maps of cell-type-specific gene expression that describe both spatiotemporal regulation of the process of oogenesis and unique transcriptomic profiles of different cell types that constitute the ovary. In this chapter, we outlined the wet-lab protocol that was followed in our recent study for sample preparation and reanalyze the resultant dataset to discuss the benchmarks in data analysis, which are fundamental to comprehensive curation of the single-cell dataset representing the fly ovary.

Host-microbiome relationships play a fundamental role in the evolution and ecology of any living being. As unicellular organisms, protists represent a unique eukaryotic model to investigate selection mechanisms of the prokaryotic microbiome at the cellular level. Field investigations are central to disentangle relative importance of selective drivers in nature. Here we performed an analysis on data from a snap-shot field study reported previously on bacterial microbiomes associated to natural populations of protist ciliates of the genus Euplotes to detect at a fine scale any influence of habitat and/or host identity in microbiome selection. Comparative analyses revealed environment at a relatively large scale (sampling area) as the main driving factor in shaping prokaryotic communities\' structures. No evidence of habitat as key-factor emerged when a smaller spatial scale was considered (pond/channel or site). When only microbiomes of ciliates from the same site were compared, a clear assessment on the influence of host identity at the species level was not achieved, probably due to the small and unbalanced number of individuals for the two considered host species. Starting from this point, wider sampling campaigns will contribute in the future to depict a general view of the drivers influencing the prokaryotic microbiomes of natural protist populations.

Since 2019, the coronavirus (COVID-19) has outbroken continuously, spreading internationally and threatening the public health. However, it was unknown how the disorder at the single-cell level was associated with the pathogenesis of COVID-19. This study presented the disorders of macrophages, epithelial cells, CD8+ T cells, and natural killer (NK) cells at the single-cell level in the courses of COVID-19 and analyzed the immune response to cytokine storm. Compared with the healthy group, patients with COVID-19 had higher proportions of macrophages and lower proportions of T and NK cells, especially proportions of macrophages and epithelial cells with an increase during patients\' conditions from mild to severe. This study suggested that there were high levels of pro-inflammatory and chemokine expressions in cells of COVID-19 and analyzed cell subsets to explore its changes and pathways. It was worth noting that several subsets of macrophages, epithelial cells, CD8 T cells, and NK cells were involved in inflammation pathways, including interleukin-17 (IL-17) signaling pathway and tumor necrosis factor (TNF) signaling pathway. Moreover, the pathways interacting COVID-19 and cytokine receptor with each other were remarkably enriched. In addition, these cell subsets played important roles in inflammation, and their abnormal functions may cause COVID-19. In conclusion, this study provided an immune outlook for COVID-19 at the single-cell level and revealed different pathways in immune response of COVID-19 single cells.

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In the past few years there has been a rapid expansion of interest in the study of single cells, especially through the new techniques that involve single-cell RNA sequencing (scRNA-seq). Recently, these techniques have provided new insights into kidney health and disease, including insights into diabetic kidney disease (DKD). However, despite the interest and the technological advances, the study of individual cells in DKD is not a new concept. Many clinicians and researchers who work within the DKD space may be familiar with experimental techniques that actually involve the study of individual cells, but may be unfamiliar with newer scRNA-seq technology. Here, with the goal of improving accessibility to the single-cell field, we provide a primer on single-cell studies with a focus on DKD. We situate the technology in its historical context and provide a brief explanation of the common aspects of the different technologies available. Then we review some of the most important recent studies of kidney (patho)biology that have taken advantage of scRNA-seq techniques, before emphasizing the new insights into the molecular pathogenesis of DKD gleaned with these techniques. Finally, we highlight common pitfalls and limitations of scRNA-seq methods and we look toward the future to how single-cell experiments may be incorporated into the study of DKD and how to interpret the findings of these experiments.