Reviews & Analysis

Metrics Reloaded is a comprehensive framework for guiding researchers in the problem-aware selection of metrics for common tasks in biomedical image analysis.

sc-SPORT offers a way to probe RNA structure at the single-cell level. It reveals cell-to-cell heterogeneity in RNA folding.

We pinpoint PCR artifacts as the primary source of inaccurate quantification in both short- and long-read RNA sequencing, a problem that intensifies with an increase in PCR cycles in both bulk and single-cell sequencing contexts. To overcome this challenge, we engineered a novel unique molecular identifier (UMI) barcode composed of homotrimer nucleotide blocks. This design facilitates accurate quantification of RNA molecules, substantially improving molecular counting.

We developed a prime editing (PE) strategy by incorporating a 5′–3′ exonuclease activity, which enhanced the efficacy and precision of ≥30-nucleotide DNA insertions without a secondary nick. Our optimization of the PE complex revealed that recruiting the exonuclease via an RNA aptamer outperformed direct protein fusions.

Intrinsically disordered regions of proteins are prevalent across the kingdoms of life; however, biophysical characterization is expensive, requiring specialized expertise and equipment and time-consuming sample preparation. By combining simulations and deep learning, we have developed a method to predict their average ensemble properties directly from sequence.

ARTR-seq uses antibody-guided in situ reverse transcription to efficiently and accurately identify RNA-binding protein target sites in as few as 20 cells, or in a formaldehyde-fixed tissue section. The high temporal resolution of ARTR-seq opens opportunities for the investigation of dynamic RNA-binding protein–RNA interactions.

In 1858, the first standard for microscope objectives was established to encourage interchangeable components. Over the following 150 years, standards have evolved to constrain the size of objectives, which limits the parameters of working distance, field of view and resolution. A new design breaks out of this conventional envelope, offering an ultra-long working distance in air and enabling new neuroscience experiments.

We have developed a framework for the analysis of multi-batch proteome profiling data using isobaric mass tags. Our framework improves quantitative accuracy and increases statistical power by accounting for known sources of variation between batches, thus enabling multiplexed proteome profiling analysis to be performed on large numbers of samples and population cohorts.

Two studies show that nanopores can identify the 20 proteinogenic amino acids and some of their post-translational modifications. Coupled with an exopeptidase, a bottom-up approach to protein sequencing using nanopores is on the horizon.

How accurate is the prediction of protein structure by AlphaFold? Terwilliger et al. address this question with a rigorous assessment of the accuracy of AlphaFold-predicted structures by comparing them with experimentally determined X-ray crystallographic data.

Two new methods enable the study of the mechanics of tissue development.

Tracking cells is a time-consuming part of biological image analysis, and traditional manual annotation methods are prohibitively laborious for tracking neurons in the deforming and moving Caenorhabditis elegans brain. By leveraging machine learning to develop a ‘targeted augmentation’ method, we substantially reduced the number of labeled images required for tracking.

We developed MAbID, a method for combined genomic profiling of histone modifications and chromatin-binding proteins in single cells, enabling researchers to study the interconnectivity between gene-regulatory mechanisms. We demonstrated MAbID’s implementation in profiling multifactorial changes in chromatin signatures during in vitro neural differentiation and in primary mouse bone marrow tissue.

Although single-cell RNA-sequencing has revolutionized biomedical research, exploring cell states from an extracellular vesicle viewpoint has remained elusive. We present an algorithm, SEVtras, that accurately captures signals from small extracellular vesicles and determines source cell-type secretion activity. SEVtras unlocks an extracellular dimension for single-cell analysis with diagnostic potential.

We developed a machine learning model, RoseTTAFoldNA, that can predict the structures of protein–DNA and protein–RNA complexes. Our model is capable of predicting accurate structures of protein families for which structural information is unknown.

Fluorescent actinometers enable the measurement of light intensity even in the depths of samples and over wide ranges of wavelengths and intensities. We introduce two protocols to quantitatively characterize the spatial distribution of light of various fluorescence imaging systems and to calibrate the illumination of commercially available instruments and light sources.

A class of protein-based molecular shape probes move us closer toward the goal of a general, genetically encoded tagging system for cryogenic electron tomography.

Single-cell inference of class-switch recombination (sciCSR) is a computational method that analyzes single-cell RNA sequencing data to deduce the temporal trajectory of how B cells develop antibody response.

A new method models mammary gland in vitro during physiological and pathological conditions.

This Perspective introduces advances in quantitative phase imaging and artificial intelligence-based image analysis and further describes how the two technologies intersect and synergize to enable biomedical research.