Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This is a compressed dataset containing 4 files:
1) "raw_count_data_filtered.txt" (1.3GB): raw read counts of genes in 12,114 mouse bulk RNA-seq samples.
2) "logCombat_UQ_log10.txt" (4.9GB): the RNA-seq data after upper-quartile (UQ) normalization, and batch effect correction using ComBat. According to our study this is in general the best processing workflow for calculating gene co-expression. Values are log10 values after addition of a small pseudo count.
3) "annotation_data.txt" (394KB): a table with the sample ID, study ID, and assigned cell type or tissue of each RNA-seq sample in this dataset.
4) "cell_types_vs_index.txt" (1.3KB): a list of each cell type and tissue in this dataset, along with their sample counts.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset has data files from 5-Choice, Pairwise Visual Discrimination (PVD), and Paired-Associates Learning (PAL) cognitive behavioral tasks in which male and female mice from 3 Alzheimer’s mouse models were tested using touchscreen technology at two different sites (e.g. University of Western Ontario and University of Guelph). Both aggregated data and trial-by-trial data are provided in this dataset.
This U.S. Geological Survey (USGS) data release represents tabular and geospatial data for the Biological Objectives for the Gulf Coast Project’s Beach Mice Bayesian network model. The data release was produced in compliance with 'open data' requirements as a way to make the scientific products associated with USGS research efforts and publications available to the public. The release consists of six items: 1. Bayesian network model that predicts the annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Tabular datasets) 2. Bayesian network model beach mice casefile (Tabular dataset) 3. Bayesian network model detection casefile (Tabular dataset) 4. Bayesian network model output of the 2009 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 5. Bayesian network model output of the 2010 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) 6. Bayesian network model output of the 2011 annual probability of beach mouse presence at a 30-m resolution in Florida coastal habitat (Raster datasets) The USGS partnered with the U.S. Fish and Wildlife Service (USFWS), the Florida Fish and Wildlife Conservation Commission, and their conservation partners to develop a Bayesian Network model that predicts the annual probability of beach mouse presence at a local (30-m) scale. The model was used to predict the annual probability of presence across a portion of the USFWS's Central Gulf and Florida Panhandle Coast Biological Planning Unit. This spatial extent included critical habitat for three endangered subspecies of beach mice (Peromyscus polionotus ssp.). The annual probability of beach mouse presence is predicted from both local and neighborhood habitat characteristics that could be influenced by management actions. The model was created using a combination of expert elicitation, simplifying assumptions, literature-derived empirical values, and a beach mouse detection and nondetection survey. When coupled with established population objectives, this study can provide insight into how much habitat is available, how much more is needed, and where conservation or restoration efforts can most efficiently achieve established objectives. The results could be used to help guide strategic habitat conservation and adaptive management of beach mice.
The Database of Physiological Parameters for Early Life Rats and Mice provides information based on scientific literature about physiological parameters. Modelers are encouraged to use the database as a starting point for researching age-specific parameter values needed in their models.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Mice is a dataset for object detection tasks - it contains Mice annotations for 7,356 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Behavioral data associated with the IBL paper: A standardized and reproducible method to measure decision-making in mice.This data set contains contains 3 million choices 101 mice across seven laboratories at six different research institutions in three countries obtained during a perceptual decision making task.When citing this data, please also cite the associated paper: https://doi.org/10.1101/2020.01.17.909838This data can also be accessed using DataJoint and web browser tools at data.internationalbrainlab.orgAdditionally, we provide a Binder hosted interactive Jupyter notebook showing how to access the data via the Open Neurophysiology Environment (ONE) interface in Python : https://mybinder.org/v2/gh/int-brain-lab/paper-behavior-binder/master?filepath=one_example.ipynbFor more information about the International Brain Laboratory please see our website: www.internationalbrainlab.comBeta Disclaimer. Please note that this is a beta version of the IBL dataset, which is still undergoing final quality checks. If you find any issues or inconsistencies in the data, please contact us at info+behavior@internationalbrainlab.org .
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This Dataset contains the raw data files of the longitudinal study about the relationship between computer mouse usage and emotional states. The files are gzipped json files.
There are 3 separate data files.
Note that the sociodemographic data is also included in the FreeMouse dataset as well as in the MouseTask dataset.
For any questions about the dataset, contact: paul.freihaut@psychologie.uni-freiburg.de
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The ReMouse dataset is collected in a guided environment
https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html
Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.
Methods Mice. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and experiments were performed in compliance with its institutional guidelines. Adult C57BL/6J mice (mus musculus) were obtained from Jackson Laboratories (#000664; Bar Harbor, ME) and were used at 4-7 months of age. Aged C57BL/6J mice were obtained from the National Institute of Aging (NIA) Rodent Aging Colony and were used at 20 months of age. For new scRNAseq experiments, female mice were used in each experiment.
Mouse injuries and single-cell isolation. To induce muscle injury, both tibialis anterior (TA) muscles of old (20 months) C57BL/6J mice were injected with 10 µl of notexin (10 µg/ml; Latoxan; France). At 0, 1, 2, 3.5, 5, or 7 days post-injury (dpi), mice were sacrificed and TA muscles were collected and processed independently to generate single-cell suspensions. Muscles were digested with 8 mg/ml Collagenase D (Roche; Switzerland) and 10 U/ml Dispase II (Roche; Switzerland), followed by manual dissociation to generate cell suspensions. Cell suspensions were sequentially filtered through 100 and 40 μm filters (Corning Cellgro #431752 and #431750) to remove debris. Erythrocytes were removed through incubation in erythrocyte lysis buffer (IBI Scientific #89135-030).
Single-cell RNA-sequencing library preparation. After digestion, single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. Cells were counted manually with a hemocytometer to determine their concentration. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, PN-1000075; Pleasanton, CA) following the manufacturer’s protocol. Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes. After preparation, libraries were sequenced using on a NextSeq 500 (Illumina; San Diego, CA) using 75 cycle high output kits (Index 1 = 8, Read 1 = 26, and Read 2 = 58). Details on estimated sequencing saturation and the number of reads per sample are shown in Sup. Data 1.
Spatial RNA sequencing library preparation. Tibialis anterior muscles of adult (5 mo) C57BL6/J mice were injected with 10µl notexin (10 µg/ml) at 2, 5, and 7 days prior to collection. Upon collection, tibialis anterior muscles were isolated, embedded in OCT, and frozen fresh in liquid nitrogen. Spatially tagged cDNA libraries were built using the Visium Spatial Gene Expression 3’ Library Construction v1 Kit (10x Genomics, PN-1000187; Pleasanton, CA) (Fig. S7). Optimal tissue permeabilization time for 10 µm thick sections was found to be 15 minutes using the 10x Genomics Visium Tissue Optimization Kit (PN-1000193). H&E stained tissue sections were imaged using Zeiss PALM MicroBeam laser capture microdissection system and the images were stitched and processed using Fiji ImageJ software. cDNA libraries were sequenced on an Illumina NextSeq 500 using 150 cycle high output kits (Read 1=28bp, Read 2=120bp, Index 1=10bp, and Index 2=10bp). Frames around the capture area on the Visium slide were aligned manually and spots covering the tissue were selected using Loop Browser v4.0.0 software (10x Genomics). Sequencing data was then aligned to the mouse reference genome (mm10) using the spaceranger v1.0.0 pipeline to generate a feature-by-spot-barcode expression matrix (10x Genomics).
Download and alignment of single-cell RNA sequencing data. For all samples available via SRA, parallel-fastq-dump (github.com/rvalieris/parallel-fastq-dump) was used to download raw .fastq files. Samples which were only available as .bam files were converted to .fastq format using bamtofastq from 10x Genomics (github.com/10XGenomics/bamtofastq). Raw reads were aligned to the mm10 reference using cellranger (v3.1.0).
Preprocessing and batch correction of single-cell RNA sequencing datasets. First, ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX). Samples were then preprocessed using the standard Seurat (v3.2.1) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat). Cells with fewer than 750 features, fewer than 1000 transcripts, or more than 30% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0) was used to identify putative doublets in each dataset, individually. BCmvn optimization was used for PK parameterization. Estimated doublet rates were computed by fitting the total number of cells after quality filtering to a linear regression of the expected doublet rates published in the 10x Chromium handbook. Estimated homotypic doublet rates were also accounted for using the modelHomotypic function. The default PN value (0.25) was used. Putative doublets were then removed from each individual dataset. After preprocessing and quality filtering, we merged the datasets and performed batch-correction with three tools, independently- Harmony (github.com/immunogenomics/harmony) (v1.0), Scanorama (github.com/brianhie/scanorama) (v1.3), and BBKNN (github.com/Teichlab/bbknn) (v1.3.12). We then used Seurat to process the integrated data. After initial integration, we removed the noisy cluster and re-integrated the data using each of the three batch-correction tools.
Cell type annotation. Cell types were determined for each integration method independently. For Harmony and Scanorama, dimensions accounting for 95% of the total variance were used to generate SNN graphs (Seurat::FindNeighbors). Louvain clustering was then performed on the output graphs (including the corrected graph output by BBKNN) using Seurat::FindClusters. A clustering resolution of 1.2 was used for Harmony (25 initial clusters), BBKNN (28 initial clusters), and Scanorama (38 initial clusters). Cell types were determined based on expression of canonical genes (Fig. S3). Clusters which had similar canonical marker gene expression patterns were merged.
Pseudotime workflow. Cells were subset based on the consensus cell types between all three integration methods. Harmony embedding values from the dimensions accounting for 95% of the total variance were used for further dimensional reduction with PHATE, using phateR (v1.0.4) (github.com/KrishnaswamyLab/phateR).
Deconvolution of spatial RNA sequencing spots. Spot deconvolution was performed using the deconvolution module in BayesPrism (previously known as “Tumor microEnvironment Deconvolution”, TED, v1.0; github.com/Danko-Lab/TED). First, myogenic cells were re-labeled, according to binning along the first PHATE dimension, as “Quiescent MuSCs” (bins 4-5), “Activated MuSCs” (bins 6-7), “Committed Myoblasts” (bins 8-10), and “Fusing Myoctes” (bins 11-18). Culture-associated muscle stem cells were ignored and myonuclei labels were retained as “Myonuclei (Type IIb)” and “Myonuclei (Type IIx)”. Next, highly and differentially expressed genes across the 25 groups of cells were identified with differential gene expression analysis using Seurat (FindAllMarkers, using Wilcoxon Rank Sum Test; results in Sup. Data 2). The resulting genes were filtered based on average log2-fold change (avg_logFC > 1) and the percentage of cells within the cluster which express each gene (pct.expressed > 0.5), yielding 1,069 genes. Mitochondrial and ribosomal protein genes were also removed from this list, in line with recommendations in the BayesPrism vignette. For each of the cell types, mean raw counts were calculated across the 1,069 genes to generate a gene expression profile for BayesPrism. Raw counts for each spot were then passed to the run.Ted function, using
The Caltech Mouse Social Interactions (CalMS21) dataset is a multi-agent dataset from behavioral neuroscience. The dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. The CalMS21 dataset is part of the Multi-Agent Behavior Challenge 2021.
To help accelerate behavioral studies, the CalMS21 dataset provides a benchmark to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabelled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of the dataset is to be able to classify behaviors accurately using both labelled and unlabelled tracking data, as well as being able to generalize to new annotators and behaviors.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains p-values and statistical significance data derived from analyzing various metabolic and dietary states in mice. The data supports research investigating the effects of diet and metabolic conditions on localized variables in specific regions of mice. The files included are:
Data Collection Methods The data was collected by analyzing correlations between variables within localized regions of the mice. These variables were consistent within individuals but showed variation dependent on dietary or metabolic states. Data collection involved the following steps: 1. Selection of experimental groups based on dietary and metabolic conditions. 2. Quantitative measurement of specific variables in localized regions of mice. 3. Statistical analysis to determine the significance of correlations across the groups.
Data Generation and Processing 1. Generation: Measurements were obtained through laboratory analysis using standardized protocols for each dietary/metabolic condition. 2. Processing: - Statistical tests were performed to identify significant correlations (e.g., t-tests, ANOVA). - P-values were computed to quantify the significance of the relationships observed. - Data was compiled into Excel sheets for organization and clarity. Technical and Non-Technical Information - Technical Details: Each file contains tabular data with headers indicating the variable pairs analyzed, their respective p-values, and the significance level (e.g., p<0.05, p<0.01).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about book subjects. It has 11 rows and is filtered where the books is Mice. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset was collected in 2020, which combines high-density Electroencephalography (HD-EEG, 128 channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes high-density resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18-33).
The EEG data were acquired using a 128-channel cap based on the standard 10/20 System with Electrical Geodesics Inc (EGI, Eugene, Oregon) system. During recording, sampling rate was 1000Hz, and the E129 (Cz) electrode was used as reference. Electrode impedances were kept below 50kohm for each electrode during the experiment.
sub-*
: EEG (.set
) and behavior data with BIDS format.
sourcedata/rawdata
: Raw .mff
EGI data and behavior data with subject information desensitization.
sourcedata/psychopy
: Stimuli and PsychoPy scripts for presentation.
derivatives/eeglab-preproc
: Preprocessed continuous EEG data with EEGLAB (Easy to set different epoch time windows for further analysis).
Please refer to the corresponding paper and GitHub code to get more details.
Chen, K., Wang, R., Huang, J., Gao, F., Yuan, Z., Qi, Y., & Wu, H. (2022). A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking. Scientific Data, 9(1), 416. https://doi.org/10.1038/s41597-022-01538-5
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896
Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Caltech Resident-Intruder Mouse dataset (CRIM13) (Burgos-Artizzu et al., CVPR 2012) consists of two mice interacting in an enclosed arena, captured by top and side view cameras at 30 Hz. We only use the top view. Seven keypoints are labeled on each mouse for a total of 14 keypoints (Segalin et al., eLife 2021).Each keypoint in the original CRIM13 dataset (https://data.caltech.edu/records/4emt5-b0t10) is labeled by five different annotators. To create the final set of labels, we take the median across all labels for each keypoint. Additionally, we remove all frames where one or both mice were absent.The labeled data are partitioned into disjoint "in-distribution" (InD) and "out-of-distribution" (OOD) sets. Each set contains different sessions / animals. We use the train/test split provided in the original dataset - the (4) resident mice are present in both InD and OOD splits; however, the intruder mouse is different for each session. The InD data contain 3986 labeled frames, and 37 unlabeled videos; the OOD data contain 1274 labeled frames, and 19 unlabeled videos.Many thanks to the authors of the CRIM13 paper who collected and analyzed the original video dataset: Xavier P. Burgos-Artizzu, Piotr Dollár, Dayu Lin, David J. Anderson and Pietro Perona.We also thank the authors of the MARS paper who collected keypoint annotations for the CRIM13 dataset: Cristina Segalin, Jalani Williams, Tomomi Karigo, May Hui, Moriel Zelikowsky, Jennifer J Sun, Pietro Perona, David J Anderson and Ann Kennedy.
Note! Data records can be downloaded individually. This is a representative sample of all data sets and data records. Raw image projections (.tiff) and tomographic sections (.bmp or ome.tiff) can be converted and imported into most 3D visualisation/quantification software such as ImageJ (https://imagej.nih.gov/ij/, NIH, USA), Arivis (Munich, Germany), Imaris (Bitplane, UK) or 3D slicer (https://www.slicer.org/). When importing multiple channels generated by OPT or LSFM the numerical value of the starting image of the projection views (OPT) or z-stacks (LSFM) should always be the same for the anatomy, insulin or GLUT2 channels. The Imaris files (.ims) contain all iso-surfaces (anatomy, insulin or GLUT2) used for quantification and numerical data mining and can be visualised for free using the Imaris viewer (https://imaris.oxinst.com/imaris-viewer). Some file names in Data record D (statistical and spatial parameter descriptions) are too long to be extracted using default Windows ZIP...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about books. It has 1 row and is filtered where the book is Mouse tales : all you never wanted to know about mice. It features 5 columns: author, publication date, book publisher, and BNB id.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
This dataset contains the results of a survey of mouse handling methods by personnel working with laboratory mice. The survey included questions about preferred handling methods, barriers to use of refined handling methods, and a knowledge quiz about refined mouse handling. Data was collected via Qualtrics survey as described in the methodology section. This dataset is associated with the following publication, accepted by PLOS One: PONE-D-23-01633R1 Title: Using refined methods to pick up mice: A survey benchmarking prevalence & beliefs about tunnel and cup handling Authors: Lauren Young, Donna Goldsteen, Elizabeth A. Nunamaker, Mark J. Prescott, Penny Reynolds, Sally Thompson-Iritani, Sarah E. Thurston, Tara L. Martin, Megan R. LaFollette
Upon weightlessness and microgravity deleterious effects on the neurosensory and neurovestibular systems haematological changes and deconditioning of musculoskeletal cardiovascular and cardiopulmonary functions have been reported. In particular loss of muscle mass and strength are triggered by weightlessness in humans during space flights that is similarly observed as a result of physical inactivity conditions and aging on Earth. However skeletal muscle tissue is of paramount importance for health maintenance (e.g. being essential to locomotion heat production and metabolism). To better prevent or eventually treat microgravity-induced muscle atrophy its underlying mechanisms have first to be characterized in detail. Using cutting-edge quantitative proteomics the aim of the present study was therefore to get an in depth view of the molecular regulations triggered by space conditions in skeletal muscles of mice during the 30-day flight of the BION-M1 biosatellite. As muscles differing in their fiber type composition appear to respond differently to microgravity (see above) we characterized here the differential response of the soleus extensor digitorum longus and vastus lateralis muscles.
Three wild-type (C57BL/6J) male mice ran on a paper spool following odor trails (Mathis et al 2018). These experiments were carried out in the laboratory of Venkatesh N. Murthy at Harvard University. Data were recorded at 30 Hz with 640 x 480 pixels resolution acquired with a Point Grey Firefly FMVU-03MTM-CS. One human annotator was instructed to localize the 12 keypoints (snout, left ear, right ear, shoulder, four spine points, tail base and three tail points). All surgical and experimental procedures for mice were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Institutional Animal Care and Use Committee. 161 frames were labeled, making this a real-world sized laboratory dataset.
This dataset contains the following: video recordings of animal behaviors, RNA-seq analysis, in vitro recordings, and fiber photometry recordings for a study on the regulation by the posterior amygdala (PA) of sexual and aggressive behaviors in male mice.
The video recordings are of all animal behaviors from two synchronized cameras on the side and top of the cage with frame by frame behavioral annotation and tracking using custom MATLAB software (https://pdollar.github.io/toolbox/). For all experiments, adult male Esr1-2A-cre mice were used. For pharmacogenetic activation, HSV-hEf1α-LS1L-Flp was injected into the MPN or VMHvl and AAV-Ef1α-fDIO-hM3Dq-mCherry was injected into the PA. Naive males were used and a non-receptive female and then a non-aggressive BALB/c male mouse were introduced to the home cage of the test mouse. For pharmacogenetic inactivation, HSV-hEf1α-LS1L-Flp was injected into the MPN or VMHvl and AAV-Ef1α-fDIO-hM4Di-mCherry was injected into the PA. The test mice first encountered a receptive female C57BL/6N mouse, and then a non-aggressive BALB/c male mouse. For both activation and inactivation experiments, animals received intraperitoneal injections of either saline or clozapine-N-oxide (CNO).
The fiber photometric recording are from male Esr1-2A-cre mice for which HSV-hEf1α-LS1L-GCaMP6f was injected into the MPN or VMHvl and an optic fiber was implanted above the posterior amygdala. Behavioral video recordings were made for these mice for which a sexually receptive female C57BL/6N mouse was placed in the home cage of the test male mouse until the resident reached ejaculation. Following this, a non-aggressive male BALB/c or C57BL/6N mouse was placed in the home cage.
The dataset for the in vitro whole-cell voltage-clamp recordings consists of recordings from the MPN or VMHvl in acute coronal brain slices from Esr1-2A-cre male adult mice. These recordings were made three weeks after the injection of AAV2-Ef1α-DIO-ChR2-EYFP into the PA and, simultaneously, injection of AAV2-hSyn-DIO-mCherry into the MPN or VMHvl. During recordings, PA terminals were stimulated using 0.5-ms blue-light pulses.
The transcriptional profiles of posterior amygdala in male C57BL/6N mice are from RNA-seq analysis of MPN- and VMHvl-projecting neurons in posterior amygdala that were microdissected by laser capture microdissection.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This is a compressed dataset containing 4 files:
1) "raw_count_data_filtered.txt" (1.3GB): raw read counts of genes in 12,114 mouse bulk RNA-seq samples.
2) "logCombat_UQ_log10.txt" (4.9GB): the RNA-seq data after upper-quartile (UQ) normalization, and batch effect correction using ComBat. According to our study this is in general the best processing workflow for calculating gene co-expression. Values are log10 values after addition of a small pseudo count.
3) "annotation_data.txt" (394KB): a table with the sample ID, study ID, and assigned cell type or tissue of each RNA-seq sample in this dataset.
4) "cell_types_vs_index.txt" (1.3KB): a list of each cell type and tissue in this dataset, along with their sample counts.