Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy.

ABSTRACT: Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments.

SUBMITTER: Waliman M

PROVIDER: S-EPMC10849476 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy.

Waliman Matthew M Johnson Ryan L RL Natesan Gunalan G Tan Shiqin S Santella Anthony A Hong Ray L RL Shah Pavak K PK

bioRxiv : the preprint server for biology 20240122

Here we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance and generalizes well to images acquired in multiple labs on multiple instruments. ...[more]

PMID: 38328064

Similar Datasets

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

Project description:Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.

| S-EPMC7153852 | biostudies-literature

Label-free hematology analysis using deep-ultraviolet microscopy.

Project description:Hematological analysis, via a complete blood count (CBC) and microscopy, is critical for screening, diagnosing, and monitoring blood conditions and diseases but requires complex equipment, multiple chemical reagents, laborious system calibration and procedures, and highly trained personnel for operation. Here we introduce a hematological assay based on label-free molecular imaging with deep-ultraviolet microscopy that can provide fast quantitative information of key hematological parameters to facilitate and improve hematological analysis. We demonstrate that this label-free approach yields 1) a quantitative five-part white blood cell differential, 2) quantitative red blood cell and hemoglobin characterization, 3) clear identification of platelets, and 4) detailed subcellular morphology. Analysis of tens of thousands of live cells is achieved in minutes without any sample preparation. Finally, we introduce a pseudocolorization scheme that accurately recapitulates the appearance of cells under conventional staining protocols for microscopic analysis of blood smears and bone marrow aspirates. Diagnostic efficacy is evaluated by a panel of hematologists performing a blind analysis of blood smears from healthy donors and thrombocytopenic and sickle cell disease patients. This work has significant implications toward simplifying and improving CBC and blood smear analysis, which is currently performed manually via bright-field microscopy, and toward the development of a low-cost, easy-to-use, and fast hematological analyzer as a point-of-care device and for low-resource settings.

| S-EPMC7334528 | biostudies-literature

Label-free live-cell imaging of nucleic acids using stimulated Raman scattering microscopy.

Project description:Imaging of nucleic acids is important for studying cellular processes such as cell division and apoptosis. A noninvasive label-free technique is attractive. Raman spectroscopy provides rich chemical information based on specific vibrational peaks. However, the signal from spontaneous Raman scattering is weak and long integration times are required, which drastically limits the imaging speed when used for microscopy. Coherent Raman scattering techniques, comprising coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) microscopy, overcome this problem by enhancing the signal level by up to five orders of magnitude. CARS microscopy suffers from a nonresonant background signal, which distorts Raman spectra and limits sensitivity. This makes CARS imaging of weak transitions in spectrally congested regions challenging. This is especially the case in the fingerprint region, where nucleic acids show characteristic peaks. The recently developed SRS microscopy is free from these limitations; excitation spectra are identical to those of spontaneous Raman and sensitivity is close to shot-noise limited. Herein we demonstrate the use of SRS imaging in the fingerprint region to map the distribution of nucleic acids in addition to proteins and lipids in single salivary gland cells of Drosophila larvae, and in single mammalian cells. This allows the imaging of DNA condensation associated with cell division and opens up possibilities of imaging such processes in vivo.

| S-EPMC3516876 | biostudies-literature

PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy.

Project description:MotivationHigh-throughput time-lapse imaging is a fundamental tool for efficient living cell profiling at single-cell resolution. Label-free phase-contrast video microscopy enables noninvasive, nontoxic, and long-term imaging. The tradeoff between speed and throughput, however, implies that despite the state-of-the-art autofocusing algorithms, out-of-focus cells are unavoidable due to the migratory nature of immune cells (velocities >10 μm/min). Here, we propose PostFocus to (i) identify out-of-focus images within time-lapse sequences with a classifier, and (ii) deploy a de-noising diffusion probabilistic model to yield reliable in-focus images.ResultsDe-noising diffusion probabilistic model outperformed deep discriminative models with a superior performance on the whole image and around cell boundaries. In addition, PostFocus improves the accuracy of image analysis (cell and contact detection) and the yield of usable videos.Availability and implementationOpen-source code and sample data are available at: https://github.com/kwu14victor/PostFocus.

| S-EPMC11520405 | biostudies-literature

Fast, label-free super-resolution live-cell imaging using rotating coherent scattering (ROCS) microscopy.

Project description:Living cells are highly dynamic systems with cellular structures being often below the optical resolution limit. Super-resolution microscopes, usually based on fluorescence cell labelling, are usually too slow to resolve small, dynamic structures. We present a label-free microscopy technique, which can generate thousands of super-resolved, high contrast images at a frame rate of 100 Hertz and without any post-processing. The technique is based on oblique sample illumination with coherent light, an approach believed to be not applicable in life sciences because of too many interference artefacts. However, by circulating an incident laser beam by 360° during one image acquisition, relevant image information is amplified. By combining total internal reflection illumination with dark-field detection, structures as small as 150 nm become separable through local destructive interferences. The technique images local changes in refractive index through scattered laser light and is applied to living mouse macrophages and helical bacteria revealing unexpected dynamic processes.

| S-EPMC4964612 | biostudies-literature

Label-free automated three-dimensional imaging of whole organs by microtomy-assisted photoacoustic microscopy.

Project description:Three-dimensional (3D) optical imaging of whole biological organs with microscopic resolution has remained a challenge. Most versions of such imaging techniques require special preparation of the tissue specimen. Here we demonstrate microtomy-assisted photoacoustic microscopy (mPAM) of mouse brains and other organs, which automatically acquires serial distortion-free and registration-free images with endogenous absorption contrasts. Without tissue staining or clearing, mPAM generates micrometer-resolution 3D images of paraffin- or agarose-embedded whole organs with high fidelity, achieved by label-free simultaneous sensing of DNA/RNA, hemoglobins, and lipids. mPAM provides histology-like imaging of cell nuclei, blood vessels, axons, and other anatomical structures, enabling the application of histopathological interpretation at the organelle level to analyze a whole organ. Its deep tissue imaging capability leads to less sectioning, resulting in negligible sectioning artifact. mPAM offers a new way to better understand complex biological organs.

| S-EPMC5680318 | biostudies-literature

Label-free and live cell imaging by interferometric scattering microscopy.

Project description:Despite recent remarkable advances in microscopic techniques, it still remains very challenging to directly observe the complex structure of cytoplasmic organelles in live cells without a fluorescent label. Here we report label-free and live-cell imaging of mammalian cell, Escherischia coli, and yeast, using interferometric scattering microscopy, which reveals the underlying structures of a variety of cytoplasmic organelles as well as the underside structure of the cells. The contact areas of the cells attached onto a glass substrate, e.g., focal adhesions and filopodia, are clearly discernible. We also found a variety of fringe-like features in the cytoplasmic area, which may reflect the folded structures of cytoplasmic organelles. We thus anticipate that the label-free interferometric scattering microscopy can be used as a powerful tool to shed interferometric light on in vivo structures and dynamics of various intracellular phenomena.

| S-EPMC5914294 | biostudies-literature

Combining microscopy with mesoscopy using optical and optoacoustic label-free modes.

Project description:Biology requires observations at multiple geometrical scales, a feature that is not typically offered by a single imaging modality. We developed a hybrid optical system that not only provides different contrast modes but also offers imaging at different geometrical scales, achieving uniquely broad resolution and a 1000-fold volume sampling increase compared to volumes scanned by optical microscopy. The system combines optoacoustic mesoscopy, optoacoustic microscopy and two-photon microscopy, the latter integrating second and third harmonic generation modes. Label-free imaging of a mouse ear and zebrafish larva ex-vivo demonstrates the contrast and scale complementarity provided by the hybrid system. We showcase the superior anatomical orientation offered by the label-free capacity and hybrid operation, over fluorescence microscopy, and the dynamic selection between field of view and resolution achieved, leading to new possibilities in biological visualization.

| S-EPMC4549672 | biostudies-other

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

Project description:MotivationReliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner.ResultsOur trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining.Availability and implementationOpen-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

| S-EPMC10563152 | biostudies-literature

Label-free detection of nanoparticles using depth scanning correlation interferometric microscopy.

Project description:Single particle level visualization of biological nanoparticles such as viruses and exosomes is challenging due to their small size and low dielectric contrast. Fluorescence based methods are highly preferred, however they require labelling which may perturb the functionality of the particle of interest. On the other hand, wide-field interferometric microscopy can be used to detect sub-diffraction limited nanoparticles without using any labels. Here we demonstrate that utilization of defocused images enhances the visibility of nanoparticles in interferometric microscopy and thus improves the detectable size limit. With the proposed method termed as Depth Scanning Correlation (DSC) Interferometric Microscopy, we experimentally demonstrate the detection of sub-35nm dielectric particles without using any labels. Furthermore, we demonstrate direct detection of single exosomes. This label-free and high throughput nanoparticle detection technique can be used to sense and characterize biological particles over a range between a few tens to a few hundred nanometers, where conventional methods are insufficient.

| S-EPMC6588623 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data