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This Registered Report presents the results of the Long-read RNA-Seq Genome Annotation Assessment Project, which is a community effort for benchmarking long-read methods for transcriptome analyses, including transcript isoform detection, quantification and de novo transcript detection.
Deconwolf is a computationally efficient and user-friendly software tool for fluorescence microscopy image deconvolution that improves the analysis of diverse fluorescence in situ hybridization methods and can handle large datasets.
scFoundation, with 100 million parameters covering about 20,000 genes, pretrained on over 50 million single-cell transcriptomics profiles, is a foundation model for diverse tasks of single-cell analysis.
Integrative and reference-informed tissue segmentation (IRIS) harnesses single-cell RNA sequencing data for the accurate identification of spatial domains in spatially resolved transcriptomics. IRIS is computationally efficient and uniquely suited for analyzing large datasets.
A fundamental mechanism for information processing in the brain is electrical activity. However, observing such activity at the single-cell level is challenging. We have developed an optical microscope that combines the advantages of targeted illumination and confocal gating to enable kilohertz-rate voltage imaging across large fields of view in thick tissue.
In targeted-illumination confocal microscopy, the combination of targeted illumination with confocal detection allows one-photon voltage imaging across large fields of view with a high signal-to-noise ratio.
Photoconversion of popular organic dyes results in blue-shifted emission and altered fluorescence lifetimes that can cause artifacts in quantitative microscopy. These can be avoided through proper labeling strategies such as using exchangeable dyes.
Comparing brain connectivity between chimpanzees and humans is a means of understanding human cognition and evolution. To address the scarcity of chimpanzee neuroimaging data, we introduce a high-quality MRI resource that reveals previously unseen anatomical details, offering valuable insights into human brain evolution.
uiPSF is a toolbox to measure point spread functions based on inverse modeling that improves single-molecule localization microscopy (SMLM) localization and microscope characterization, and that works for many microscopy technologies.
uiPSF (universal inverse modeling of point spread functions) offers a versatile solution for inferring accurate PSF models from images of beads and blinking fluorophores, enabling improved imaging and image processing in localization microscopy.
This resource presents a high-resolution diffusion MRI dataset of a chimpanzee brain, which should be useful for evolutionary studies of primate brain evolution.
PdCO is a switchable optogenetic tool for inhibiting synaptic transmission in neuronal terminals in vivo, as demonstrated in a variety of contexts mainly in the mouse.
An experimental method to study how cells sense and react to external mechanical forces combines controlled mechanical stimulation using nanopipettes with fluorescence imaging of membrane tension. This approach facilitates the study of mechanosensitive ion channels and the propagation of cell membrane tension.
FluidFM-based force-controlled nanopipettes enable control of mechanical stimuli for the investigation of Piezo1-induced mechanosensation in cell membranes.
Dimension reduction helps to visualize high-dimensional datasets. These tools should be used thoughtfully and with tuned parameters. Sometimes, these methods take a second thought.
Sometimes their queer identity is one that people set apart from their science identity. Others find unique ways to integrate multiple facets of their identity.
Using a dependency-aware deep generative framework, spaVAE efficiently models spatially resolved transcriptomics data and advances diverse analysis tasks. Following similar strategies, spaPeakVAE and spaMultiVAE enable spatial ATAC-seq data and spatial multi-omics data modeling and analysis, respectively.
Spatial transcriptomics and mRNA splicing measurements encode rich spatiotemporal information for cell states and their transitions. We present a multiscale dynamical system method for reconstructing cell-state-specific dynamics and spatial state transitions. This theory-based approach reconciles short-timescale local tensor streamlines between cells with long-timescale transition paths that connect cell attractors.