Pub Date : 2024-09-30DOI: 10.1038/s41592-024-02428-x
Christian A. Hanke, John D. Westbrook, Benjamin M. Webb, Thomas-O. Peulen, Catherine L. Lawson, Andrej Sali, Helen M. Berman, Claus A. M. Seidel, Brinda Vallat
{"title":"Making fluorescence-based integrative structures and associated kinetic information accessible","authors":"Christian A. Hanke, John D. Westbrook, Benjamin M. Webb, Thomas-O. Peulen, Catherine L. Lawson, Andrej Sali, Helen M. Berman, Claus A. M. Seidel, Brinda Vallat","doi":"10.1038/s41592-024-02428-x","DOIUrl":"10.1038/s41592-024-02428-x","url":null,"abstract":"","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02428-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1038/s41592-024-02440-1
Enny H van Beest, Célian Bimbard, Julie M J Fabre, Sam W Dodgson, Flóra Takács, Philip Coen, Anna Lebedeva, Kenneth D Harris, Matteo Carandini
Neural activity spans multiple time scales, from milliseconds to months. Its evolution can be recorded with chronic high-density arrays such as Neuropixels probes, which can measure each spike at tens of sites and record hundreds of neurons. These probes produce vast amounts of data that require different approaches for tracking neurons across recordings. Here, to meet this need, we developed UnitMatch, a pipeline that operates after spike sorting, based only on each unit's average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. Across the brain, neurons had distinctive inter-spike interval distributions. Their correlations with other neurons remained stable over weeks. In the visual cortex, the neurons' selectivity for visual stimuli remained similarly stable. In the striatum, however, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal both invariance and plasticity in neural activity across days.
{"title":"Tracking neurons across days with high-density probes.","authors":"Enny H van Beest, Célian Bimbard, Julie M J Fabre, Sam W Dodgson, Flóra Takács, Philip Coen, Anna Lebedeva, Kenneth D Harris, Matteo Carandini","doi":"10.1038/s41592-024-02440-1","DOIUrl":"https://doi.org/10.1038/s41592-024-02440-1","url":null,"abstract":"<p><p>Neural activity spans multiple time scales, from milliseconds to months. Its evolution can be recorded with chronic high-density arrays such as Neuropixels probes, which can measure each spike at tens of sites and record hundreds of neurons. These probes produce vast amounts of data that require different approaches for tracking neurons across recordings. Here, to meet this need, we developed UnitMatch, a pipeline that operates after spike sorting, based only on each unit's average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. Across the brain, neurons had distinctive inter-spike interval distributions. Their correlations with other neurons remained stable over weeks. In the visual cortex, the neurons' selectivity for visual stimuli remained similarly stable. In the striatum, however, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal both invariance and plasticity in neural activity across days.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1038/s41592-024-02436-x
Mark S Keller, Ilan Gold, Chuck McCallum, Trevor Manz, Peter V Kharchenko, Nils Gehlenborg
Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://vitessce.io .
{"title":"Vitessce: integrative visualization of multimodal and spatially resolved single-cell data.","authors":"Mark S Keller, Ilan Gold, Chuck McCallum, Trevor Manz, Peter V Kharchenko, Nils Gehlenborg","doi":"10.1038/s41592-024-02436-x","DOIUrl":"https://doi.org/10.1038/s41592-024-02436-x","url":null,"abstract":"<p><p>Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://vitessce.io .</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1038/s41592-024-02424-1
Ekaterina Kazantseva, Ataberk Donmez, Maria Frolova, Mihai Pop, Mikhail Kolmogorov
Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Short-read approaches can be used to detect small-scale variation between strains but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy, an algorithm for strain-level metagenome assembly and phasing from Nanopore and PacBio reads. Strainy takes a de novo metagenomic assembly as input and identifies strain variants, which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and PacBio metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with PacBio-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct strain distribution and mutational patterns in bacterial species. This work presents Strainy, a long-read metagenome assembler that allows the identification of strain distributions and mutational patterns in environmental metagenomes.
{"title":"Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing","authors":"Ekaterina Kazantseva, Ataberk Donmez, Maria Frolova, Mihai Pop, Mikhail Kolmogorov","doi":"10.1038/s41592-024-02424-1","DOIUrl":"10.1038/s41592-024-02424-1","url":null,"abstract":"Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Short-read approaches can be used to detect small-scale variation between strains but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy, an algorithm for strain-level metagenome assembly and phasing from Nanopore and PacBio reads. Strainy takes a de novo metagenomic assembly as input and identifies strain variants, which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and PacBio metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with PacBio-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct strain distribution and mutational patterns in bacterial species. This work presents Strainy, a long-read metagenome assembler that allows the identification of strain distributions and mutational patterns in environmental metagenomes.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm. This Analysis study compares computational methods for single-cell multi-omics prediction and integration, generating useful insights for method users and developers working with different analysis purposes and biological problems.
{"title":"Benchmarking algorithms for single-cell multi-omics prediction and integration","authors":"Yinlei Hu, Siyuan Wan, Yuanhanyu Luo, Yuanzhe Li, Tong Wu, Wentao Deng, Chen Jiang, Shan Jiang, Yueping Zhang, Nianping Liu, Zongcheng Yang, Falai Chen, Bin Li, Kun Qu","doi":"10.1038/s41592-024-02429-w","DOIUrl":"10.1038/s41592-024-02429-w","url":null,"abstract":"The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm. This Analysis study compares computational methods for single-cell multi-omics prediction and integration, generating useful insights for method users and developers working with different analysis purposes and biological problems.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1038/s41592-024-02396-2
Andre C. Stiel, Vasilis Ntziachristos
Optoacoustic (photoacoustic) imaging advances allow high-resolution optical imaging much deeper than optical microscopy. However, while label-free optoacoustics have already entered clinical application, biological imaging is in need of ubiquitous optoacoustic labels for use in ways that are similar to how fluorescent proteins propelled optical microscopy. We review photoswitching advances that shine a new light or, in analogy, ‘bring a new sound’ to biological optoacoustic imaging. Based on engineered labels and novel devices, switching uses light or other energy forms and enables signal modulation and synchronous detection for maximizing contrast and detection sensitivity over other optoacoustic labels. Herein, we explain contrast enhancement in the spectral versus temporal domains and review labels and key concepts of switching and their properties to modulate optoacoustic signals. We further outline systems and applications and discuss how switching can enable optoacoustic imaging of cellular or molecular contrast at depths and resolutions beyond those of other optical methods. This Review describes how photoswitchable probes and associated hardware innovations are poised to transform optoacoustic imaging in life sciences research.
{"title":"Controlling the sound of light: photoswitching optoacoustic imaging","authors":"Andre C. Stiel, Vasilis Ntziachristos","doi":"10.1038/s41592-024-02396-2","DOIUrl":"10.1038/s41592-024-02396-2","url":null,"abstract":"Optoacoustic (photoacoustic) imaging advances allow high-resolution optical imaging much deeper than optical microscopy. However, while label-free optoacoustics have already entered clinical application, biological imaging is in need of ubiquitous optoacoustic labels for use in ways that are similar to how fluorescent proteins propelled optical microscopy. We review photoswitching advances that shine a new light or, in analogy, ‘bring a new sound’ to biological optoacoustic imaging. Based on engineered labels and novel devices, switching uses light or other energy forms and enables signal modulation and synchronous detection for maximizing contrast and detection sensitivity over other optoacoustic labels. Herein, we explain contrast enhancement in the spectral versus temporal domains and review labels and key concepts of switching and their properties to modulate optoacoustic signals. We further outline systems and applications and discuss how switching can enable optoacoustic imaging of cellular or molecular contrast at depths and resolutions beyond those of other optical methods. This Review describes how photoswitchable probes and associated hardware innovations are poised to transform optoacoustic imaging in life sciences research.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1038/s41592-024-02417-0
Ruiming Cao, Nikita S Divekar, James K Nuñez, Srigokul Upadhyayula, Laura Waller
Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.
{"title":"Neural space-time model for dynamic multi-shot imaging.","authors":"Ruiming Cao, Nikita S Divekar, James K Nuñez, Srigokul Upadhyayula, Laura Waller","doi":"10.1038/s41592-024-02417-0","DOIUrl":"https://doi.org/10.1038/s41592-024-02417-0","url":null,"abstract":"<p><p>Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.</p>","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142350514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1038/s41592-024-02418-z
Kim Fabiano Marquart, Nicolas Mathis, Amina Mollaysa, Saphira Müller, Lucas Kissling, Tanja Rothgangl, Lukas Schmidheini, Péter István Kulcsár, Ahmed Allam, Masako M. Kaufmann, Mai Matsushita, Tatjana Haenggi, Toni Cathomen, Manfred Kopf, Michael Krauthammer, Gerald Schwank
Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB for application in mammalian cells (TnpBmax), leading to an average 4.4-fold improvement in editing. In addition, we developed variants mutated at position K76 that recognize alternative target-adjacent motifs (TAMs), expanding the targeting range of ISDra2 TnpB. We further generated an extensive dataset on TnpBmax editing efficiencies at 10,211 target sites. This enabled us to delineate rules for on-target and off-target editing and to devise a deep learning model, termed TnpB editing efficiency predictor (TEEP; https://www.tnpb.app ), capable of predicting ISDra2 TnpB guiding RNA (ωRNA) activity with high performance (r > 0.8). Employing TEEP, we achieved editing efficiencies up to 75.3% in the murine liver and 65.9% in the murine brain after adeno-associated virus (AAV) vector delivery of TnpBmax. Overall, the set of tools presented in this study facilitates the application of TnpB as an ultracompact programmable endonuclease in research and therapeutics. This work introduces engineered TnpBmax proteins with enhanced efficiency and an expanded targeting range. By leveraging an extensive dataset on editing efficiencies, it also integrates a deep learning model to predict guide RNA activity.
{"title":"Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs","authors":"Kim Fabiano Marquart, Nicolas Mathis, Amina Mollaysa, Saphira Müller, Lucas Kissling, Tanja Rothgangl, Lukas Schmidheini, Péter István Kulcsár, Ahmed Allam, Masako M. Kaufmann, Mai Matsushita, Tatjana Haenggi, Toni Cathomen, Manfred Kopf, Michael Krauthammer, Gerald Schwank","doi":"10.1038/s41592-024-02418-z","DOIUrl":"10.1038/s41592-024-02418-z","url":null,"abstract":"Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB for application in mammalian cells (TnpBmax), leading to an average 4.4-fold improvement in editing. In addition, we developed variants mutated at position K76 that recognize alternative target-adjacent motifs (TAMs), expanding the targeting range of ISDra2 TnpB. We further generated an extensive dataset on TnpBmax editing efficiencies at 10,211 target sites. This enabled us to delineate rules for on-target and off-target editing and to devise a deep learning model, termed TnpB editing efficiency predictor (TEEP; https://www.tnpb.app ), capable of predicting ISDra2 TnpB guiding RNA (ωRNA) activity with high performance (r > 0.8). Employing TEEP, we achieved editing efficiencies up to 75.3% in the murine liver and 65.9% in the murine brain after adeno-associated virus (AAV) vector delivery of TnpBmax. Overall, the set of tools presented in this study facilitates the application of TnpB as an ultracompact programmable endonuclease in research and therapeutics. This work introduces engineered TnpBmax proteins with enhanced efficiency and an expanded targeting range. By leveraging an extensive dataset on editing efficiencies, it also integrates a deep learning model to predict guide RNA activity.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1038/s41592-024-02460-x
Similarly to CRISPR–Cas systems, TnpB proteins from bacterial transposons can be employed as RNA-guided endonucleases for genome editing. By combining rational protein design and machine learning, ISDra2 TnpB variants with enhanced editing efficiency and a broader targeting range were developed, along with a prediction tool to design effective guiding RNAs.
{"title":"Expanding the genome editing toolbox with designer CRISPR–Cas-like transposons","authors":"","doi":"10.1038/s41592-024-02460-x","DOIUrl":"10.1038/s41592-024-02460-x","url":null,"abstract":"Similarly to CRISPR–Cas systems, TnpB proteins from bacterial transposons can be employed as RNA-guided endonucleases for genome editing. By combining rational protein design and machine learning, ISDra2 TnpB variants with enhanced editing efficiency and a broader targeting range were developed, along with a prediction tool to design effective guiding RNAs.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1038/s41592-024-02411-6
Helen Farrants, Yichun Shuai, William C. Lemon, Christian Monroy Hernandez, Deng Zhang, Shang Yang, Ronak Patel, Guanda Qiao, Michelle S. Frei, Sarah E. Plutkis, Jonathan B. Grimm, Timothy L. Hanson, Filip Tomaska, Glenn C. Turner, Carsen Stringer, Philipp J. Keller, Abraham G. Beyene, Yao Chen, Yajie Liang, Luke D. Lavis, Eric R. Schreiter
Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM). WHaloCaMP is a chemigenetic calcium indicator that can be combined with different rhodamine dyes for multiplexed or FLIM imaging in vivo, as demonstrated for calcium imaging in neuronal cultures, brain slices, Drosophila, zebrafish larvae and the mouse brain.
{"title":"A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging","authors":"Helen Farrants, Yichun Shuai, William C. Lemon, Christian Monroy Hernandez, Deng Zhang, Shang Yang, Ronak Patel, Guanda Qiao, Michelle S. Frei, Sarah E. Plutkis, Jonathan B. Grimm, Timothy L. Hanson, Filip Tomaska, Glenn C. Turner, Carsen Stringer, Philipp J. Keller, Abraham G. Beyene, Yao Chen, Yajie Liang, Luke D. Lavis, Eric R. Schreiter","doi":"10.1038/s41592-024-02411-6","DOIUrl":"10.1038/s41592-024-02411-6","url":null,"abstract":"Genetically encoded fluorescent calcium indicators allow cellular-resolution recording of physiology. However, bright, genetically targetable indicators that can be multiplexed with existing tools in vivo are needed for simultaneous imaging of multiple signals. Here we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several that efficiently label the brain in animals. When bound to a near-infrared dye-ligand, WHaloCaMP shows a 7× increase in fluorescence intensity and a 2.1-ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a to image Ca2+ responses in vivo in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae and to quantify Ca2+ concentration using fluorescence lifetime imaging microscopy (FLIM). WHaloCaMP is a chemigenetic calcium indicator that can be combined with different rhodamine dyes for multiplexed or FLIM imaging in vivo, as demonstrated for calcium imaging in neuronal cultures, brain slices, Drosophila, zebrafish larvae and the mouse brain.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":null,"pages":null},"PeriodicalIF":36.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41592-024-02411-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142291695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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