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Accurate isolation of targeted extracellular vesicle (EV) is challenging due to the heterogeneity of EV subpopulations which are from different cell origins. Most EV subpopulations lack a single marker whose expression cleanly distinguish them from mixed populations of closely related EVs. Herein, we developed a modular platform capable of taking multiple binding events as input, logic computations, and producing two independent outputs for tandem microchips for EV subpopulation isolation. Taking advantages of the excellent selectivity of dual-aptamer recognition-mediated DNA computation and the sensitivity of tandem microchips, this method achieves, for the first time, sequential isolation of tumour PD-L1 EVs and non-tumour PD-L1 EVs. As a result, the developed platform can not only effectively distinguish cancer patients from healthy donors, but also provides new clues for assessing immune heterogeneity. Moreover, the captured EVs can be released through a DNA hydrolysis reaction with high efficiency, which is compatible with downstream mass spectrometry for EV proteome profiling. Overall, this strategy is expected to isolate different EV subpopulations, translate EVs into reliable clinical biomarkers, and accurately investigate the biological functions of different EV subsets.

Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

Exosomal programmed cell death ligand 1 (exoPD-L1) has emerged as a promising biomarker for cancer diagnosis and immunotherapy outcome prediction. However, the existing quantitation methods are incapable of addressing the heterogeneity of exoPD-L1 glycosylation, which has been demonstrated to be the institutional basis for PD-L1/PD-1 interaction and the crucial participant in inhibiting the activity of CD8

Liquid biopsy offers non-invasive and real-time molecular profiling of individual patients, and is thus considered a revolutionary technology in precision medicine. Exosomes have been acknowledged as significant biomarkers in liquid biopsy, as they play a central role in cell-cell communication and are closely related to the pathogenesis of most human malignancies. Nevertheless, in biofluids exosomes always co-exist with other particles, and the cargo components of exosomes are highly heterogeneous. Thus, the isolation and molecular characterization of exosomes are still technically challenging. Microfluidics technology effectively addresses this challenge by virtue of its inherent advantages, such as precise manipulation of fluids, low consumption of samples and reagents, and a high level of integration. Recent advances in microfluidics allow in situ exosome capture and molecular detection with unprecedented selectivity and sensitivity. In this review, the state-of-the-art developments in microfluidics-based exosome research, including exosome isolation approaches and molecular detection strategies, with highlights of the characterization of exosomal biomarkers in cancer liquid biopsy is summarized. The major challenges are also discussed and some perspectives for the future directions of exosome-based liquid biopsy in microfluidic systems are presented.

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.

Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.