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publicJul 2021View details →

Rapid mechanical stimulation of inner-ear hair cells by photonic pressure

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publicMar 2022View details →

Emergence of a geometric pattern of cell fates from tissue-scale mechanics in the Drosophila eye

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publicMay 2023View details →

Mechanical manipulation of cancer cell tumorigenicity via heat shock protein signaling

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publicMay 2021View details →

Midbrain dopaminergic inputs gate amygdala intercalated cell clusters by distinct and cooperative mechanisms in male mice

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opencc-by-4.0Aug 1996View details →

METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia. in Bite-force estimation for Tyrannosaurus rex from tooth-marked bones

METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia.

opencc-zeroDec 2015View details →

Data from: Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

In order to achieve selective targeting of affinity–ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor–ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

opencc-by-4.0Oct 2023View details →

Datasets associated with the manuscript "Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques"

meta_data_TCR-pMHC_from_STCRDab.tsv, TCR-pMHC structures used for contacts analysis.tcr_gliph_input_sars2.tsv, input files (TCR sequences and related information) used for clustering TCRs targeting SARS-CoV-2 epitopes and epitope-unknown TCRs.tcr_gliph_input_non-sars2.tsv, input files used for clustering TCRs targeting non-SARS-CoV-2 epitopes and epitope-unknown TCRs.tcr_gliph_output*, output files from the GLIPH software, including the recognized TCR clusters by GLIPH (convergence-group.txt), the linkage information of TCR clusters (clone-network.txt), and the recognized motif in TCR clusters (kmer.txt).md_trajs.tar, structures and MD simulation trajectories of TCR-614-pMHC and TCR-204-pMHC complexes.

openSep 2023View details →

Single-cell multi-omics analysis identifies context specific gene regulatory gates and mechanisms

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states.  In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g., AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies.  We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omic data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

opencc-by-4.0Apr 2024View details →

Infection of HSV1 in innate lymphoid cells leads to alienation of the mechanism of T/B-cell activation in the adaptive immune response-image

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opencc-by-4.0Apr 2022View details →

Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (10/16)

Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer et al. (2022) ‘Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810]. <table> <tbody> <tr> <td> Description </td> <td> Location </td> </tr> <tr> <td> Centralized GitHub repository with: <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer et al.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <a href="https://github.com/CoulonLab/Keizer-et-al">Keizer et al.</a> repository </td> </tr> <tr> <td> Raw microscopy data: <ul> <li>Experiments performed with the 30’-PR scheme</li> <li>Experiment performed with the 100”-PR scheme</li> <li>Experiment performed with high frame rate (dt = 0.5”)</li> </ul> </td> <td> <a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR) <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR) <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR) <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR) <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR) <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR) <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR) <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR) <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5") <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR) </td> </tr> <tr> <td> Concatenated TIFFs and timestamp files for all of the 30’-PR data. </td> <td> <a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2) <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2) </td> </tr> <tr> <td> Python pipeline to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell. </td> <td> <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository </td> </tr> <tr> <td> <ul> <li>Final registered and rotated TIFF files: <ul> <li>30’-PR experiments: n = 35 cells</li> <li>100”-PR experiment, including time projections & kymograph</li> <li>dt = 0.5” experiments: n = 3 cells</li> <li>no force: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li>Data files with trajectories and force time profiles for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <a href="https://zenodo.org/record/6510207">Zenodo 13</a> </td> </tr> <tr> <td> Single-MNPs fluorescence: raw data, Python/Fiji scripts and instructions </td> <td> <a href="https://zenodo.org/record/6510209">Zenodo 14</a> </td> </tr> <tr> <td> <ul> <li>MagSim, Python library for magnetic simulations</li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository </td> </tr> <tr> <td> Force calibration – Method 1: Gradient of free GFP-ferritin in solution <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <a href="https://zenodo.org/record/4627062">Zenodo 15</a> </td> </tr> <tr> <td> Force calibration – Method 2: Attraction of ferritin-coated beads (Fig. S7) <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <a href="https://zenodo.org/record/6510211">Zenodo 16</a> </td> </tr> <tr> <td> <ul> <li>Python library for force inference using different polymer models</li> </ul> </td> <td> <a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository </td> </tr> </tbody> </table> License: All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646">Author Accepted Manuscript</a> of the study [Keizer et al. 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810">Final Published Version</a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).   Overview of the raw data repositories (Zenodo 1-10) Refer to the Material and Methods section of the article for details on data production. Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains: <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the _preInjection, _1-pre-inj or _1-inj_1 suffix)</li> <li>These positions were imaged again after injection (suffix _postInjection, _2-post-inj or _1-inj_2) and before the magnet was added (suffix _beforeexp or _before-attr)</li> <li>They were imaged again with the magnet added (suffix _attraction1). If acquisition was stopped and restarted an extra folder is created (suffix _attraction2)</li> <li>They were then imaged after the magnet was removed (suffix _release1)</li> <li>Finally, the cells were monitored after the experiment (suffix _after-exp or _postexp)</li> </ul> </li> <li>A text file named lab_journal_[...].txt contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul>   Overview of the concatenated datasets (Zenodo 11-12) In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains: <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li>path (Relative path): Reference to the original (raw MicroManager) file</li> <li>start_time (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li>time_in_file (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li>start_time_s (seconds): Variable start_time converted to a number of seconds</li> <li>time (seconds): Sum of start_time and time_in_file</li> <li>timestamp (Timestamp): Variable time, back-converted to a timestamp</li> <li>timeOn (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li>timeOff (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li>forceActivated (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li>seconds_since_first_magnet_ON (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li>Frame (Integer) Frame number (1-indexed)</li> <li>Positions (Integer): The position number</li> </ul> </li> </ul>   Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16) These datasets and their analysis are fully described in the Materials and Methods section of the article and in the different README.md files within the various folders of the datasets.

opencc-zeroDec 2017View details →

Data from: A novel mechanism of gland formation in zebrafish involving transdifferentiation of renal epithelial cells and live cell extrusion

Transdifferentiation is the poorly understood phenomenon whereby a terminally differentiated cell acquires a completely new identity. Here, we describe a rare example of a naturally occurring transdifferentiation in zebrafish in which kidney distal tubule epithelial cells are converted into an endocrine gland known as the Corpuscles of Stannius (CS). We find that this process requires Notch signalling and is associated with the cytoplasmic sequestration of the Hnf1b transcription factor, a master-regulator of renal tubule fate. A deficiency in the Irx3b transcription factor results in ectopic transdifferentiation of distal tubule cells to a CS identity but in a Notch-dependent fashion. Using live-cell imaging we show that CS cells undergo apical constriction en masse and are then extruded from the tubule to form a distinct organ. This system provides a valuable new model to understand the molecular and morphological basis of transdifferentiation and will ad vance efforts to exploit this rare phenomenon therapeutically.

opencc-zeroJul 2021View details →

Mechanical heterogeneity along single cell-cell junctions is driven by lateral clustering of cadherins during vertebrate axis elongation

Morphogenesis is governed by the interplay of molecular signals and mechanical forces across multiple length scales. The last decade has seen tremendous advances in our understanding of the dynamics of protein localization and turnover at sub-cellular length scales, and at the other end of the spectrum, of mechanics at tissue-level length scales. Integrating the two remains a challenge, however, because we lack a detailed understanding of the subcellular patterns of mechanical properties of cells within tissues. Here, in the context of the elongating body axis of Xenopus embryos, we combine tools from cell biology and physics to demonstrate that individual cell-cell junctions display finely-patterned local mechanical heterogeneity along their length. We show that such local mechanical patterning is essential for the cell movements of convergent extension and is imparted by locally patterned clustering of a classical cadherin. Finally, the patterning of cadherins and thus local mechanics along cell-cell junctions are controlled by Planar Cell Polarity signaling, a key genetic module for CE that is mutated in diverse human birth defects.

ClinicalTrials.gov28/100

Mechanisms of Chronic Kidney Disease (CKD)-Induced Foam Cell Formation

ClinicalTrials.gov study NCT01671605. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov28/100

Mechanisms and Targeted Therapy of Airway Basal Cell Dysfunction in Bronchiolitis Obliterans Syndrome

ClinicalTrials.gov study NCT07018804. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

publicJun 2018View details →

Data from: A cell-based mechanical model of coronary artery tunica media

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publicNov 2018View details →

Data from: A novel mechanism of gland formation in zebrafish involving transdifferentiation of renal epithelial cells and live cell extrusion

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