Cell-type-specific co-expression inference from single cell RNA-sequencing data

Molecular models of bidirectional promoter regulation

Approximately 11% of human genes are transcribed by a bidirectional promoter (BDP), defined as two genes with <1 kb between their transcription start sites. Despite their evolutionary conservation and enrichment for housekeeping genes and oncogenes, the regulatory role of BDPs remains unclear. BDPs have been suggested to facilitate gene coregulation and/or decrease expression noise. This review discusses these potential regulatory functions through the context of six prospective underlying mechanistic models: a single nucleosome free region, shared transcription factor/regulator binding, cooperative negative supercoiling, bimodal histone marks, joint activation by enhancer(s), and RNA-mediated recruitment of regulators. These molecular mechanisms may act independently and/or cooperatively to facilitate the coregulation and/or decreased expression noise predicted of BDPs.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.

Electrochemiluminescence Analysis of Multiple Glycans on Single Living Cell with a Closed Bipolar Electrode Array Chip

The profiling of multiple glycans on a single cell is important for elucidating glycosylation mechanisms and accurately identifying disease states. Herein, we developed a closed bipolar electrode (BPE) array chip for live single-cell trapping and in situ galactose and sialic acid detection with the electrochemiluminescence (ECL) method. Methylene blue-DNA (MB-DNA) as well as biotin-DNA (Bio-DNA) codecorated AuNPs were prepared as nanoprobes, which were selectively labeled on the cell surface through chemoselective labeling techniques. The individual cell was captured and labeled in the microtrap of the cathodic chamber, under an appropriate potential, MB molecules on the cellular membrane underwent oxidation, triggering the reduction of [Ru(bpy)3]2+/TPA and consequently generating ECL signals in the anodic chamber. The abundance of MB groups on the single cell enabled selective monitoring of both sialic acid and galactosyl groups with high sensitivity using ECL. The sialic acid and galactosyl content per HepG2 cell were detected to be 0.66 and 0.82 fmol, respectively. Through comprehensive evaluation of these two types of glycans on a single cell, tumor cells, and normal cells could be effectively discriminated and the accuracy of single-cell heterogeneous analysis was improved. Additionally, dynamic monitoring of variations in galactosyl groups on the surface of the single cell was also achieved. This work introduced a straightforward and convenient approach for heterogeneity analysis among single cells.