@article{14257, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Michalska, Julia M and Lyudchik, Julia and Velicky, Philipp and Korinkova, Hana and Watson, Jake and Cenameri, Alban and Sommer, Christoph M and Amberg, Nicole and Venturino, Alessandro and Roessler, Karl and Czech, Thomas and Höftberger, Romana and Siegert, Sandra and Novarino, Gaia and Jonas, Peter M and Danzl, Johann G}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {1051--1064}, publisher = {Springer Nature}, title = {{Imaging brain tissue architecture across millimeter to nanometer scales}}, doi = {10.1038/s41587-023-01911-8}, volume = {42}, year = {2024}, } @article{12106, abstract = {Regulation of chromatin states involves the dynamic interplay between different histone modifications to control gene expression. Recent advances have enabled mapping of histone marks in single cells, but most methods are constrained to profile only one histone mark per cell. Here, we present an integrated experimental and computational framework, scChIX-seq (single-cell chromatin immunocleavage and unmixing sequencing), to map several histone marks in single cells. scChIX-seq multiplexes two histone marks together in single cells, then computationally deconvolves the signal using training data from respective histone mark profiles. This framework learns the cell-type-specific correlation structure between histone marks, and therefore does not require a priori assumptions of their genomic distributions. Using scChIX-seq, we demonstrate multimodal analysis of histone marks in single cells across a range of mark combinations. Modeling dynamics of in vitro macrophage differentiation enables integrated analysis of chromatin velocity. Overall, scChIX-seq unlocks systematic interrogation of the interplay between histone modifications in single cells.}, author = {Yeung, Jake and Florescu, Maria and Zeller, Peter and De Barbanson, Buys Anton and Wellenstein, Max D. and Van Oudenaarden, Alexander}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {813–823}, publisher = {Springer Nature}, title = {{scChIX-seq infers dynamic relationships between histone modifications in single cells}}, doi = {10.1038/s41587-022-01560-3}, volume = {41}, year = {2023}, } @article{7889, abstract = {Autoluminescent plants engineered to express a bacterial bioluminescence gene cluster in plastids have not been widely adopted because of low light output. We engineered tobacco plants with a fungal bioluminescence system that converts caffeic acid (present in all plants) into luciferin and report self-sustained luminescence that is visible to the naked eye. Our findings could underpin development of a suite of imaging tools for plants.}, author = {Mitiouchkina, Tatiana and Mishin, Alexander S. and Gonzalez Somermeyer, Louisa and Markina, Nadezhda M. and Chepurnyh, Tatiana V. and Guglya, Elena B. and Karataeva, Tatiana A. and Palkina, Kseniia A. and Shakhova, Ekaterina S. and Fakhranurova, Liliia I. and Chekova, Sofia V. and Tsarkova, Aleksandra S. and Golubev, Yaroslav V. and Negrebetsky, Vadim V. and Dolgushin, Sergey A. and Shalaev, Pavel V. and Shlykov, Dmitry and Melnik, Olesya A. and Shipunova, Victoria O. and Deyev, Sergey M. and Bubyrev, Andrey I. and Pushin, Alexander S. and Choob, Vladimir V. and Dolgov, Sergey V. and Kondrashov, Fyodor and Yampolsky, Ilia V. and Sarkisyan, Karen S.}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {944--946}, publisher = {Springer Nature}, title = {{Plants with genetically encoded autoluminescence}}, doi = {10.1038/s41587-020-0500-9}, volume = {38}, year = {2020}, } @article{7181, abstract = {Multiple sequence alignments (MSAs) are used for structural1,2 and evolutionary predictions1,2, but the complexity of aligning large datasets requires the use of approximate solutions3, including the progressive algorithm4. Progressive MSA methods start by aligning the most similar sequences and subsequently incorporate the remaining sequences, from leaf-to-root, based on a guide-tree. Their accuracy declines substantially as the number of sequences is scaled up5. We introduce a regressive algorithm that enables MSA of up to 1.4 million sequences on a standard workstation and substantially improves accuracy on datasets larger than 10,000 sequences. Our regressive algorithm works the other way around to the progressive algorithm and begins by aligning the most dissimilar sequences. It uses an efficient divide-and-conquer strategy to run third-party alignment methods in linear time, regardless of their original complexity. Our approach will enable analyses of extremely large genomic datasets such as the recently announced Earth BioGenome Project, which comprises 1.5 million eukaryotic genomes6.}, author = {Garriga, Edgar and Di Tommaso, Paolo and Magis, Cedrik and Erb, Ionas and Mansouri, Leila and Baltzis, Athanasios and Laayouni, Hafid and Kondrashov, Fyodor and Floden, Evan and Notredame, Cedric}, issn = {1546-1696}, journal = {Nature Biotechnology}, number = {12}, pages = {1466--1470}, publisher = {Springer Nature}, title = {{Large multiple sequence alignments with a root-to-leaf regressive method}}, doi = {10.1038/s41587-019-0333-6}, volume = {37}, year = {2019}, }