Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data

Processed Datasets for: EGA Study ID: EGAS00001006141 EGA Dataset ID: EGAD00001008662   Find processed files and arrow files   Abstract: Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8+ TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.

opencc-zeroMay 2023View details →

African elephants address one another with individually specific name-like calls

<div> Personal names are a universal feature of human language, yet few analogs exist in other species. While dolphins and parrots address conspecifics by imitating the calls of the addressee, human names are not imitations of the sounds typically made by the named individual. Labeling objects or individuals without relying on imitation of the sounds made by that object or individual radically expands the expressive power of language. Thus, if non-imitative name analogs were found in other species, this could have important implications for our understanding of language evolution. Here, we present evidence that wild African elephants address one another with individually specific calls without imitation. We used machine learning to demonstrate that the receiver of a call could be predicted from the call's acoustic structure and found little evidence that calls addressed to a given receiver imitate the receiver's own calls. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual, indicating that they can determine from a call's structure if it was addressed to them. Our findings offer the first evidence for a non-human species individually addressing conspecifics without imitating the receiver. </div>

Data and code associated with the paper 'Mode-Specific Coupling of Nanoparticle-on-Mirror Cavities with Cylindrical Vector Beams'

Data and code associated with the following paper: <a href="https://doi.org/10.1021/acs.nanolett.3c00561">V. Vento et al, Nano Lett. 2023</a> A thorough explanation of the experiment performed is available there. The name of each sub-folder and file in Maps_data_code.zip indicates the corresponding figure number ("FIG #") and the type of content ("raw_data", "data", "plot", "analysis", "calculation", "simulation"). The Raman maps data are analyzed through the script Raman_maps_analysis.m. The photoluminescence maps data in the supplementary information are analyzed through the script PL_maps_analysis.m.  Used softwares: Matlab R2021a, Python 3.9, Comsol Multiphysics 5.6

opencc-zeroMay 2023View details →

On ageing and age-specific effects of spontaneous mutations

Evolutionary theories of ageing assume causal mutations either have beneficial early-life effects which gradually become deleterious with advancing age (antagonistic pleiotropy: AP) or mutations that only have deleterious effects at old age (mutation accumulation: MA). Mechanistically, ageing is predicted to result from damage accumulating in the soma. While this scenario is compatible with AP, it is not immediately obvious how damage would accumulate under MA. In a modified version of the MA theory, it has been suggested that mutations with weakly deleterious effects at young age can also contribute to ageing, if they generate damage that gradually accumulates with age. Mutations with increasing deleterious effects have recently gained support from theoretical work and studies of large-effect mutations. Here we address if spontaneous mutations also have negative effects that increase with age. We accumulate mutations with early-life effects in Drosophila melanogaster across 27 generations and compare their relative effects on fecundity early and late in life. Our mutation accumulation lines on average have substantially lower early-life fecundity compared to controls. These effects were further maintained throughout life, but they did not increase with age. Our results thus suggest that most spontaneous mutations do not contribute to damage accumulation and ageing.

opencc-by-4.0Feb 2023View details →

Figure 6 The age-specific predation rate k in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 6 The age-specific predation rate k (x), and age-specific net predation rateqx() of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetrany-

opencc-by-4.0Apr 2023View details →

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material

Figure S1: Distribution of the reviewed 141 BRCA1 missense variants; Figure S2: The Shapely values for the BRCA1 XGBoost models; Figure S3: The Shapely values of the BRCA1 XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.

Dataset for "Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback"

Dataset for the manuscript entitled: Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback. DOI of the manuscript: <a href="https://doi.org/10.1126/sciadv.adh1328">10.1126/sciadv.adh1328</a> Abstract of the manuscript: Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics. Description of the variables in the data storage dictionary: <ol> <li>cursor_positions: sequence of the virtual cursor position over a session</li> <li>range_of_rewardable_cursor_position: range of cursor positions that can be rewarded. Upper threshold excluded. Lower threshold included.  </li> <li>cursor_times: timing of the cursor positions provided by the cursor_positions data, in the same clock as spike and lick times. </li> <li>lick_times: timing of all recorded licks.  </li> <li>reward_times:timing of the opening of the valve that releases the water reward. </li> <li>master_spike_times: time of the spikes of the master neurons.</li> <li>master_spike_shape: spike shape of each spike stored in master_spike_times. The shipe shapes are shown for 3s (30kHz sampling rate), for each 4 electrode of the corresponding tetrode.</li> <li>neighbor_spike_times': same as master_spike_times for neighbor neurons.</li> <li>neighbor_spike_shape': same as master_spike_shape for neighbor neurons.   </li> </ol> General structure of the data set: Each variable is a hierarchical tree of lists: [Protocol][Mouse][Session].  Protocol takes one of the following values: 0: Bar feedback | 1: Full shuffle | 2: No Feedback | 5: Playback Structured Feedback | 7: Spontaneous activity | 8: Barrel Shuffle | 9: Frame shuffle. Mouse and Session iterate through respectively the mice that were involved in the protocole, and the sessions, generally 5 in total. Python code to load the hdf5 File The code below relies on libraries available on a standard, mac anaconda install of the jupyter notebook system on the 25/03/2024. It provides with several nested lists of Numpy arrays. For instance, to access the series of cursor positions of Mouse M during session number S of Protocole P, index as follows:  CP = cursor_positions_DATA[P][M][S] # 1 - Load the libraries     import h5py     import numpy as np     import pylab as pl     filename = "./data.h5"     f = h5py.File(filename, "r") # 2 - Extraction of the cursor position, time, lick and reward time, as well as the activity of the master and neighbor neurons.      cursor_positions = f['cursor_positions']     cursor_positions_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[cursor_positions[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         cursor_positions_DATA.append(DATA_protocole)       cursor_times = f['cursor_times']     cursor_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[cursor_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         cursor_times_DATA.append(DATA_protocole)       lick_times = f['lick_times']     lick_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[lick_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         lick_times_DATA.append(DATA_protocole)       reward_times = f['reward_times']     reward_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[reward_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         reward_times_DATA.append(DATA_protocole)       master_spike_times = f['master_spike_times']     master_spike_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[master_spike_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     DATA_unit = []                     for Unit in range(len(S)):                         if not(list(S) == [0,1]):                             N = f[S[Unit][0]]                             bfr = np.array(N)                             DATA_unit.append(bfr)                      DATA_mouse.append(DATA_unit)                 DATA_protocole.append(DATA_mouse)         master_spike_times_DATA.append(DATA_protocole)       neighbor_spike_times = f['neighbor_spike_times']     neighbor_spike_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[neighbor_spike_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     DATA_unit = []                     for Unit in range(len(S)):                         if not(list(S) == [0,1]):                             N = f[S[Unit][0]]                             bfr = np.array(N)                             DATA_unit.append(bfr)                      DATA_mouse.append(DATA_unit)                 DATA_protocole.append(DATA_mouse)         neighbor_spike_times_DATA.append(DATA_protocole)

opencc-by-4.0Apr 2023View details →

Memory or naive T cell origin of VZV gE-specific T cell clonotypes boosted by zoster vaccination.

Subjects were vaccinated with either the live attenuated zoster vaccine (n=5) or recombinant zoster vaccine (n=10). Peripheral blood mononuclear cells were obtained before vaccination (baseline), at peak response approximately one month after vaccination, and after 5 years. The TRB sequences for VZV gE-specific T cell clonotypes were identified from CD4 T cells that proliferated in response to overlapping peptides spanning the VZV gE protein. To assess if the vaccines recruited responses from existing memory or from naive T cells, VZV gE-specific clonotypes were cross-matched to naive and memory CD4 T cell subsets isolated from baseline PBMC. This dataset contains the raw TRB sequences obtained using the Adaptive Biotechnologies Immunoseq platform for: 1) peak gE clonotypes 2) year 5 gE clonotypes 3) baseline memory CD4 T cell subsets 4) baseline naive CD4 T cell subsets.

Dataset 1 for "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses"

Dataset with 9 host species (min. 5 study sites and min 45 sampled individuals sampled per site) for the first part of the analysis in "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses". One file contains the data table. One file contains a table with descriptions of the columns in the data table.

[Dataset] Pattern and repeatability of ascarid-specific antigen excretion through chicken faeces, and the diagnostic accuracy of copro-antigen measurements as compared with McMaster egg counts and plasma and egg yolk antibody measurements in laying hens

Comprehensive data examining the pattern and repeatability of ascarid-specific antigen excretion in chicken faeces and the diagnostic accuracy of copro-antigen measurements compared to McMaster egg counts and antibody measurements in laying hens. The dataset consists of observations and measurements obtained from a controlled study involving laying hens infected with mixed Ascaridia galli and Heterakis gallinarum. A total of 179 individual hens were monitored between wpi 2 and 18 and their fecal samples/blood samples were collected at specific time points. Faecel samples were repeatedly collected four(4) consecutive times in one wpi.Hence antigen measurements is 4 X 179 = 716 measurements

opencc-by-4.0Jun 2023View details →

Data and code to reproduce analysis in Sacchi et al. 2023: Sex-specific fitness consequences of mate change in Scopoli's shearwaters (Calonectris diomedea)

This data package contains data and code needed to reproduce the analysis reported in Sex-specific fitness consequences of mate change in Scopoli’s shearwaters (Calonectris diomedea), doi 10.1016/j.anbehav.2023.05.017 Included are: 1) Readme file - description of all other files and variables described in datasets 2) Appendix.Rmd = R code file containing all the analysis reported in the paper 3) breed.txt = this data table contains data for the analysis of fitness/breeding success 4) skip.txt = this data table contains data for the analysis of skipping behaviour 5) females.txt = this file contains data in headed format to build CR models for the female population. Covariate indicates if an individual's life history started with event 1 (partner known, first partner) or with event 3 (partner unknown). 6) males.txt = this file contains data in headed format to build CR models for the male population. Covariate indicates if an individual's life history started with event 1 (partner known, first partner) or with event 3 (partner unknown). 7) gepat.pat = this file contains the matrix design necessary to run CR models with e-surge (version 2.2.3)

opencc-by-4.0Dec 2022View details →

Cancer-specific association between Tau (MAPT) and cellular pathways, clinical outcome, and drug response

To bring new evidence that Tau represents a key protein in cancer, we present an in silico pan-cancer analysis of MAPT transcriptomic profile in over 10000 clinical samples and over 1300 pre-clinical samples provided by the TCGA and the DEPMAP datasets respectively.

opencc-by-4.0Jan 2023View details →

Data used in the study "mRNA vaccination boosts spike-specific T cell memory and promotes expansion of CD45RAint TEMRA-like CD8+ T cells in COVID-19 recovered individuals"

Data associated with an original research study examining T cell responses to mRNA vaccination in COVID-19 recovered individuals. 10X Cell Ranger outputs, bulk TCR sequencing data, and T cell functional (ICS) data in this study have been deposited. Authors KMB and HR contributed equally to this effort. Address correspondence to EWN.

opencc-zeroJul 2023View details →

Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5-18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, as well as in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleen in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end-stage of an inflammatory disorder following lymphocyte-directed therapy. These findings also underscore the importance of studying T cells in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and post-transplant T cell landscape.

opencc-zeroJul 2023View details →

Sex-specific effects of psychoactive pollution on behavioural individuality and plasticity in fish

The global rise of pharmaceutical contaminants in the aquatic environment poses a serious threat to ecological and evolutionary processes. Studies have traditionally focused on the collateral (average) effects of psychoactive pollutants on ecologically-relevant behaviours of wildlife, often neglecting effects among and within individuals, and whether they differ between males and females. We tested whether psychoactive pollutants have sex-specific effects on behavioural individuality and plasticity in guppies (Poecilia reticulata), a freshwater species that inhabits contaminated waterways in the wild. Fish were exposed to fluoxetine (Prozac) for two years across multiple generations before their activity and stress-related behaviour were repeatedly assayed. Using a Bayesian statistical approach that partitions the effects among and within individuals, we found that males—but not females—in fluoxetine-exposed populations differed less from each other in their behaviour (lower behavioural individuality) than unexposed males. In sharp contrast, effects on behavioural plasticity were observed in females—but not in males—whereby exposure to even low levels of fluoxetine resulted in a substantial decrease (activity) and increase (freezing behaviour) in the behavioural plasticity of females. Our evidence reveals that psychoactive pollution has sex-specific effects on the individual behaviour of fish, suggesting that males and females might not be equally vulnerable to global pollutants.

openmit-licenseJul 2023View details →

Data for investigation of cell-type-specific expression and regulation in Setbp1 (p.S858R) atypical SGS male mice

processed.tar.gz, contains all files from the data directory associated with this projects and includes the following: <ul> <li>setbp1_targets.csv : list of human SETP1 gene targets from ChIP-seq experiments converted to mouse and enriched for additional interactions from literature references (https://pubmed.ncbi.nlm.nih.gov/35685777/ and https://www.nature.com/articles/s41467-018-04462-8,    </li> <li>seurat_objects/ <ul> <li>kidney_integrated_celltypes.Rdata : filtered and preprocessed seurat object with cell type assignments for input to SoupX processing</li> <li>kidney_integrated_celltypes_postSoup.Rdata : preprocessed and quality controlled seurat object with final cell type assignments after SoupX processing</li> <li>setbp1_cerebralintcelltypes.Rdata : preprocessed and quality controlled seurat object with final cell type assignments</li> </ul> </li> <li>decoupleR_CollecTRI/ <ul> <li>mouse_prior_tri.csv : accessed May 2023, it is unclear how versioning or accessions are used so we provided the Mouse CollecTRI (https://github.com/saezlab/CollecTRI) used in this study to calculate cell-type-specific TF activity</li> </ul> </li> <li>motif_inputs/ <ul> <li>Mus_motif_all.txt : TF-motif PANDA input for mm10 reference genome enriched for</li> </ul> </li> <li>expression_inputs/ : expression matrices for all cell types for both tissues and conditions (n = 50) used to construct cell-type-specific PANDA networks</li> </ul> The below files are from the data/results directory of this associated project and include the following: <ul> <li>alpaca.tar.gz : directory containing differential community analysis outputs from ALPACA</li> <li>seurat.tar.gz : directory containing SoupX intermediates, as well as the</li> </ul>

opencc-zeroAug 2023View details →

Pre-fertilisation gamete thermal environment influences reproductive success, unmasking opposing sex-specific responses in Atlantic Salmon

The environment gametes perform in just before fertilisation is increasingly recognised to affect offspring fitness, yet the contributions of male and female gametes and their adaptive significance remain largely unexplored. Here, we investigated gametic thermal plasticity and its effects on hatching success and embryo performance in Atlantic salmon (Salmo salar). Eggs and sperm were incubated overnight at 2°C or 8°C, temperatures within the optimal thermal range of this species. Crosses between warm- and cold-incubated gametes were compared using a fully-factorial design, with half of each clutch reared in cold temperatures and the other in warm temperatures. This allowed disentangling single-sex interaction effects when pre-fertilisation temperature of gametes mismatched embryonic conditions. Pre-fertilisation temperature influenced the age at hatch, and matching sperm and embryo temperatures resulted in earlier hatching. Warm incubation benefited eggs but harmed sperm, reducing the hatching success and, overall, gametic thermal plasticity did not enhance offspring fitness, indicating vulnerability to thermal changes. We highlight the sensitivity of male gametes to higher temperatures, and that gamete acclimation may not effectively buffer against deleterious effects of thermal fluctuations. From an applied angle, we propose the differential storage of male and female gametes as a tool to enhance sustainability within the hatcheries.

opencc-by-4.0Aug 2023View details →

Hepatitis B surface antigen reduction is associated with hepatitis B core-specific CD8+ T cell quality

The file containing post QC, count matrix, containing 6 samples as following. (S01:CHBN001, S02:CHBN002, S03:CHBN003, S04:CHBN004, S12:CHBN005, S14:CHBN006.) scRNAseq Libraries generated by 10xGenomics 5'-kit were read by NovaSeq 6000 platform.  After sequencing, raw reads were mapped to human generated by cellranger 6.1.2, then generated count matrix were subjected to QC according to Seurat manual (mitochondrial genes <10%, ribosomal genes > 0.05%), then SCT-transformed and integrated with 3000 features. Detail of QC/integration will be described in our manuscript.

opencc-by-4.0Aug 2023View details →

Data Files for "Low-Temperature Specific Heat Capacity of Water--Ammonia Mixtures Down to the Eutectic"

The zip file contains all data files and plots used and created in the publication: Low-Temperature Specific Heat Capacity of Water--Ammonia Mixtures Down to the Eutectic