Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network

A tissue sample is a valuable resource for understanding a patient's symptoms and health status in relation to tumor growth. Recent research seeks to establish a connection between tissue-specific tumor samples and genetic markers (genes). This breakthrough has paved the way for personalized cancer therapies. With this motivation, the proposed model constructs a heterogeneous network based on tumor sample-gene relation data and gene-gene interaction data. This network also incorporates tissue-specific gene expression and primary site-based gene counts as features, enabling tissue-specific predictions. Graph neural networks (GNNs) have proven effective in modeling complex interactions and predicting links within this network. The proposed model has successfully predicted tumor-gene associations by leveraging sampling-based GNNs and link layer embedding. The model's performance metrics, such as AUC-ROC scores, reached approximately 94%, demonstrating the potential of this heterogeneous network in predicting tissue-specific tumor sample-gene links. This paper's findings highlight the importance of tissue-specific associations in cancer research.

Direct male development in chromosomally ZZ zebrafish

The genetics of sex determination varies across taxa, sometimes even within a species. Major domesticated strains of zebrafish (Danio rerio), including AB and TU, lack a strong genetic sex determining locus, but strains more recently derived from nature, like Nadia (NA), possess a ZZ male/ZW female chromosomal sex-determination system. AB fish pass through a juvenile ovary stage, forming oocytes that survive in fish that become females but die in fish that become males. To understand mechanisms of gonad development in NA zebrafish, we studied histology and single cell transcriptomics in developing ZZ and ZW fish. ZW fish developed oocytes by 22 days post-fertilization (dpf) but ZZ fish directly formed testes, avoiding a juvenile ovary phase. Gonads of some ZW and WW fish, however, developed oocytes that died as the gonad became a testis, mimicking AB fish, suggesting that the gynogenetically derived AB strain is chromosomally WW. Single-cell RNA-seq of 19dpf gonads showed similar cell types in ZZ and ZW fish, including germ cells, precursors of gonadal support cells, steroidogenic cells, interstitial/stromal cells, and immune cells, consistent with a bipotential juvenile gonad. In contrast, scRNA-seq of 30dpf gonads revealed that cells in ZZ gonads had transcriptomes characteristic of testicular Sertoli, Leydig, and germ cells while ZW gonads had granulosa cells, theca cells, and developing oocytes. Hematopoietic and vascular cells were similar in both sex genotypes. These results show that juvenile NA zebrafish initially develop a bipotential gonad; that a factor on the NA W chromosome, or fewer than two Z chromosomes, is essential to initiate oocyte development; and without the W factor, or with two Z doses, NA gonads develop directly into testes without passing through the juvenile ovary stage. Sex determination in AB and TU strains mimics NA ZW and WW zebrafish, suggesting loss of the Z chromosome during domestication. Genetic analysis of the NA strain will facilitate our understanding of the evolution of sex determination mechanisms.

Adaptive Regularized Tri-Factor Non-Negative Matrix Factorization for Cell Type Deconvolution

Motivation

Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type mRNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions.

Results

To address these challenges, we introduce an Adaptive Regularized Tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art reference-free methods. In a real-world application, our method accurately estimates cell proportions, as evidenced by the nearly perfect Pearson's correlation between ARTdeConv estimates and flow cytometry measurements in a dataset from a trivalent influenza vaccine study. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies.

Availability and implementation

The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners at https://github.com/gr8lawrence/ARTDeConv .

Direct Male Development in Chromosomally ZZ Zebrafish

The genetics of sex determination varies across taxa, sometimes even within a species. Major domesticated strains of zebrafish ( Danio rerio ), including AB and TU, lack a strong genetic sex determining locus, but strains more recently derived from nature, like Nadia (NA), possess a ZZ male/ZW female chromosomal sex-determination system. AB strain fish pass through a juvenile ovary stage, forming oocytes that survive in fish that become females but die in fish that become males. To understand mechanisms of gonad development in NA zebrafish, we studied histology and single cell transcriptomics in developing ZZ and ZW fish. ZW fish developed oocytes by 22 days post-fertilization (dpf) but ZZ fish directly formed testes, avoiding a juvenile ovary phase. Gonads of some ZW and WW fish, however, developed oocytes that died as the gonad became a testis, mimicking AB fish, suggesting that the gynogenetically derived AB strain is chromosomally WW. Single-cell RNA-seq of 19dpf gonads showed similar cell types in ZZ and ZW fish, including germ cells, precursors of gonadal support cells, steroidogenic cells, interstitial/stromal cells, and immune cells, consistent with a bipotential juvenile gonad. In contrast, scRNA-seq of 30dpf gonads revealed that cells in ZZ gonads had transcriptomes characteristic of testicular Sertoli, Leydig, and germ cells while ZW gonads had granulosa cells, theca cells, and developing oocytes. Hematopoietic and vascular cells were similar in both sex genotypes. These results show that juvenile NA zebrafish initially develop a bipotential gonad; that a factor on the NA W chromosome or fewer than two Z chromosomes is essential to initiate oocyte development; and without the W factor or with two Z doses, NA gonads develop directly into testes without passing through the juvenile ovary stage. Sex determination in AB and TU strains mimics NA ZW and WW zebrafish, suggesting loss of the Z chromosome during domestication. Genetic analysis of the NA strain will facilitate our understanding of the evolution of sex determination mechanisms.

Cell-type-specific metabolism in plants

Every plant organ contains tens of different cell types, each with a specialized function. These functions are intrinsically associated with specific metabolic flux distributions that permit the synthesis of the ATP, reducing equivalents and biosynthetic precursors demanded by the cell. Investigating such cell-type-specific metabolism is complicated by the mosaic of different cells within each tissue combined with the relative scarcity of certain types. However, techniques for the isolation of specific cells, their analysis in situ by microscopy, or modeling of their function in silico have permitted insight into cell-type-specific metabolism. In this review we present some of the methods used in the analysis of cell-type-specific metabolism before describing what we know about metabolism in several cell types that have been studied in depth; (i) leaf source and sink cells; (ii) glandular trichomes that are capable of rapid synthesis of specialized metabolites; (iii) guard cells that must accumulate large quantities of the osmolytes needed for stomatal opening; (iv) cells of seeds involved in storage of reserves; and (v) the mesophyll and bundle sheath cells of C4 plants that participate in a CO₂ concentrating cycle. Metabolism is discussed in terms of its principal features, connection to cell function and what factors affect the flux distribution. Demand for precursors and energy, availability of substrates and suppression of deleterious processes are identified as key factors in shaping cell-type-specific metabolism.

Isthmin-1 (Ism1) modulates renal branching morphogenesis and mesenchyme condensation during early kidney development

The outgrowth of epithelial bud followed by reiterated bifurcations during renal development is driven by the ligand-receptor interactions between the epithelium and the surrounding mesenchyme. Here, by exploring ligand-receptor interactions in E10.5 and E11.5 kidneys by single cell RNA-seq, we find that Isthmin1 (Ism1), a secreted protein, resembles Gdnf expression and modulates kidney branching morphogenesis. Mice deficient for Ism1 exhibit defective ureteric bud bifurcation and impaired metanephric mesenchyme condensation in E11.5 embryos, attributable to the compromised Gdnf/Ret signaling, ultimately leading to renal agenesis and hypoplasia/dysplasia. By HRP-induced proximity labelling, we further identify integrin α8β1 as a receptor of Ism1 in E11.5 kidney and demonstrate that Ism1 promoted cell-cell adhesion through interacting with Integrin α8β1, the receptor whose activation is responsible for Gdnf expression and mesenchyme condensation. Taken together, our work reveals Ism1 as a critical regulator of cell-cell interaction that modulates Gdnf/Ret signaling during early kidney development.

Self-supervised learning of cell type specificity from immunohistochemical images

Motivation

Advances in bioimaging now permit in situ proteomic characterization of cell–cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image.

Results

We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics.

Availability and implementation

Code and trained model are available at www.github.com/murphy17/HPA-SimCLR.

Supplementary information

Supplementary data are available at Bioinformatics online.

LRcell: detecting the source of differential expression at the sub-cell-type level from bulk RNA-seq data

Given most tissues are consist of abundant and diverse (sub-)cell types, an important yet unaddressed problem in bulk RNA-seq analysis is to identify at which (sub-)cell type(s) the differential expression occurs. Single-cell RNA-sequencing (scRNA-seq) technologies can answer the question, but they are often labor-intensive and cost-prohibitive. Here, we present LRcell, a computational method aiming to identify specific (sub-)cell type(s) that drives the changes observed in a bulk RNA-seq experiment. In addition, LRcell provides pre-embedded marker genes computed from putative scRNA-seq experiments as options to execute the analyses. We conduct a simulation study to demonstrate the effectiveness and reliability of LRcell. Using three different real datasets, we show that LRcell successfully identifies known cell types involved in psychiatric disorders. Applying LRcell to bulk RNA-seq results can produce a hypothesis on which (sub-)cell type(s) contributes to the differential expression. LRcell is complementary to cell type deconvolution methods.