Recent Advances in single-cell Bioinformatics

Bulk-omic assays have revolutionized biomedical research and clinical practice. Unfortunately, they produce a single dataset from highly complex samples, capturing just the average signal from hundreds of thousands or even millions of different cells. Single-cell -omics were established as alternative approaches, enabling us to achieve high granularity and resolution. During this past decade, fueled by the cost reduction of next-generation sequencing and technological advancements, single-cell approaches enabled the characterization of large cell populations, and the development of multi-omic assays capturing diverse dimensions, such as gene expression, proteins, immune cell receptor sequences, epigenetic, or genetic information, as well as large scale functional screening. Single-cell assays shifted the focus from cell types to cell states and populations, thus expanding our understanding of cellular and molecular biology.

Moreover, tissue architecture and cell localization support countless research and clinical decisions worldwide. However, this information is lost in bulk or single-cell assays. To this end, spatial tissue profiling was recently developed to bring high plexity interrogation in situ, promising to inaugurate a new era of tissue investigation single-handedly. Spatial tissue profiling enables us to go from a single or a handful of stains and antibodies to dozens, hundreds, or even thousands of genes quantified at the micrometer or even nanometer resolution.

Despite the tremendous technological advancements in single-cell and spatial-omics, their utility is constrained by our ability to process, store, visualize, analyze, integrate, and ingest the vast generated data. In silico bioinformatics and image analysis methods render these assays valuable and actionable, maximizing their utility in research, innovation, and healthcare.

This Research Topic celebrates and invites in silico innovations and foundational work that make single-cell and spatial analyses possible and valuable. We want to provide a venue for researchers who design and implement novel approaches for single-cell and spatial data analysis across all manifestations of these powerful assays, including sequencing-, imaging-, and mass spectrometry-based methods, for data integration, and downstream analyses. Foundational work, standards, benchmarking efforts, and approaches supporting storing, indexing, querying, compressing, and visualizing spatial and single cell modalities are also welcome.

We look forward to establishing a space where ground-breaking and foundational work is presented side-by-side, empowering the community to maximize the utility of these exciting assays. To that end, we encourage submissions in bioinformatics algorithms, computational methods, image analysis methods, pipelines, platforms, tools, and benchmarks, including but not limited to the following areas:

● Single-cell transcriptomics
● Spatial -omics
● Single-cell epigenomics, proteomics, multi-omics
● Single-cell image analysis, segmentation, and processing
● Single-cell segmentation and tracking in "cell movie" videos
● Machine learning methods in single-cell data analysis
● Single-cell/spatial data methods for processing, visualization, integration, deconvolution
● Automated pipelines
● Benchmarking of single cell/spatial data analysis methods
● Privacy-preserving methods for distributed single-cell data analysis at scale

as well as new molecular mechanisms/biology revealed by single-cell technologies.

Frontiers in Bioinformatics is one of our newest open-access journals, led by Prof. Adam Godzik (University of California Riverside). The journal provides a forum where new discoveries in all aspects of bioinformatics and the analysis of biological data are presented. Frontiers in Bioinformatics focuses on new bioinformatics tools and novel applications that can bring new insights to specific biological problems, efforts that cross standard field boundaries.