Volume 20 Issue 2, February 2023

In academia, job-hunting couples can face difficult two-body challenges. But many universities offer options.

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

Dramatic advances in protein structure prediction have sparked debate as to whether the problem of predicting structure from sequence is solved or not. Here, I argue that AlphaFold2 and its peers are currently limited by the fact that they predict only a single structure, instead of a structural distribution, and that this realization is crucial for the next generation of structure prediction algorithms.

Two research groups independently develop tools to record the history of cellular dynamics.

A new twist on expansion microscopy called Magnify uses a mechanically sturdy gel to simultaneously anchor and expand diverse biological samples for super-resolution imaging.

To gain insight into cell function, researchers are tracking the cytoskeleton and its parts, such as actin. They combine methods, find new trackers and validate them.

A deep learning approach called DeepPiCt facilitates segmentation and macromolecular identification in the cellular jungle of electron cryotomography data.

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.

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.

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.

This Perspective discusses available software tools for lipidomics data analysis and provides a web-based Lipidomics Tools Guide to help guide the choice of these tools, organized by the major tasks for lipidomics research.

The AlphaFill algorithm transplants missing small molecules and ions from experimentally determined structures to predicted protein models in the AlphaFold protein structure database. All AlphaFill entries are available for visual inspection and download through the AlphaFill website.

LILAC is a photoactivatable version of Lifeact, a tool for labeling F-actin. LILAC can help avoid cytotoxicity, which is sometimes associated with the use of Lifeact.

This work presents a computational framework, COMMOT, to spatially infer cell–cell communication from transcriptomics data based on a variant of optimal transport (OT).

This paper presents nonnegative spatial factorization, a general framework for spatially aware and interpretable dimension reduction for high-dimensional spatial data, and its application to spatial transcriptomics analysis.

A computational pipeline for haplotype-aware pantranscriptome analysis has been developed, which enables spliced pangenome graph construction, RNA sequencing data alignment, and estimation of haplotype-specific transcript expression levels.

Hybrid Unmixing offers enhanced imaging of multiplexed fluorescence labels, enabling longitudinal imaging of multiple fluorescent signals with reduced illumination intensities.

This analysis compares the performance of seven algorithms for cluster analysis of single-molecule localization microscopy data. The results provide a framework for comparing these types of methods and point users to the best tools.

Cryogenic correlated light, ion and electron microscopy (cryo-CLIEM) integrates three-dimensional confocal microscopy with focused ion beam–scanning electron microscopy for efficient preparation of lamellae containing target structures for in situ structural biology with cryo-electron tomography.

The ELI-TriScope advances cryo-CLEM by focusing light, electron and ion beams on cryopreserved samples for markedly improved preparation of cryo-lamellae containing target structures.

DeePiCt (deep picker in context) is a versatile, open-source deep-learning framework for supervised segmentation and localization of subcellular organelles and biomolecular complexes in cryo-electron tomography.

During segmentation of neurons in electron microscopy datasets, auxiliary learning via the prediction of local shape descriptors increases efficiency, which is important for the processing of datasets of ever-increasing size.

MARIO is a robust tool for accurately matching multimodal single-cell datasets.