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Although structural variation is less explored than single-nucleotide variation, recent studies have shown it to be associated with several human diseases. Three fresh computational methods might help to elucidate this inadequately understood part of our genetic makeup.
This Review describes advances in cryogenic electron tomography on focused ion beam lamellae, highlighting the key benefits of this technology for in situ structural biology and discussing important future directions.
Optimal design of spatial transcriptomic experiments allows statistical evaluation of the impact of various biological and technological features on the discovery of cell phenotypes.
This Review covers the state of the art in applying mass spectrometry- or next-generation sequencing-based techniques for single-cell proteomics analysis, offering suggestions for maximizing the advantages of both approaches.
A community of researchers working in the emerging field of single-cell proteomics propose best-practice experimental and computational recommendations and reporting guidelines for studies analyzing proteins from single cells by mass spectrometry.
Stimulated Raman scattering (SRS) microscopy has the capability to simultaneously visualize the spatial distribution of different biomolecules, but it remains challenging to reach super-resolution. To achieve this goal, a deconvolution algorithm, A-PoD, was developed and combined with SRS microscopy, enabling examination of nanoscopic biomolecular distribution and subcellular metabolic activity in cells and tissues.
Communication between cells is crucial for coordinated cellular functions in multicellular organisms. We present an optimal transport theory-based tool to infer cell–cell communication networks, spatial signaling directions and downstream targets in multicellular systems from spatial gene expression data.
A deep learning approach called DeepPiCt facilitates segmentation and macromolecular identification in the cellular jungle of electron cryotomography data.
Alignment of single-cell proteomics data across platforms is difficult when different data sets contain limited shared features, as is typical in single-cell assays with antibody readouts. Therefore, we developed matching with partial overlap (MARIO) to enable confident and accurate matching for multimodal data integration and cross-species analysis.
Dimension reduction is a cornerstone of exploratory data analysis; however, traditional methods fail to preserve the spatial context of spatial genomics data. In this work, we develop a nonnegative spatial factorization (NSF) model that allows interpretable, parts-based decomposition of spatial single-cell count data. NSF allows label-free annotation of regions of interest in spatial genomics data and identifies genes and cells that can be used to define those regions.
We developed an advanced deep learning approach called local shape descriptors (LSDs) to enable analysis of large electron microscopy datasets with increased efficiency. This technique will speed processing of future petabyte-sized datasets and democratize connectomics research by enabling these analyses using modest computational infrastructure available to most laboratories.
