Browse
Submit Data
Databases
API
Help

Dataset Information

32 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

ABSTRACT: Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.

SUBMITTER: Kim DNH

PROVIDER: S-EPMC8484462 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Label-free classification of cells based on supervised machine learning of subcellular structures.

Project description:It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.

| S-EPMC6350988 | biostudies-literature

Label-free classification of neurons and glia in neural stem cell cultures using a hyperspectral imaging microscopy combined with machine learning.

Project description:Due to a growing demand for a viable label-free observation method in the biomedical field, many techniques, such as quantitative phase imaging and Raman spectroscopy, have been studied, and a complementary approach, hyperspectral imaging, has also been introduced. We developed a high-speed hyperspectral imaging microscopy imaging method with commercially available apparatus, employing a liquid crystal tunable bandpass filter combined with a pixel-wise machine learning classification. Next, we evaluated the feasibility of the application of this method for stem cell research utilizing neural stem cells. Employing this microscopy method, with a 562 × 562 μm2 field of view, 2048 × 2048 pixel resolution images containing 63 wavelength pixel-wise spectra could be obtained in 30 seconds. The neural stem cells were differentiated into neurons and astroglia (glia), and a four-class cell classification evaluation (including neuronal cell body, glial cell body, process and extracellular region) was conducted under co-cultured conditions. As a result, an average of 88% of the objects of interest were correctly classified, with an average precision of 94%, and more than 99% of the extracellular pixels were correctly segregated. These results indicated that the proposed hyperspectral imaging microscopy is feasible as a label-free observation method for stem cell research.

| S-EPMC6345994 | biostudies-literature

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Project description:Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

| S-EPMC10681317 | biostudies-literature

PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning.

Project description:Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.

| S-EPMC6363787 | biostudies-literature

Deep Learning in Label-free Cell Classification.

Project description:Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

| S-EPMC4791545 | biostudies-literature

Digital holographic microscopy: a quantitative label-free microscopy technique for phenotypic screening.

Project description:Digital Holographic Microscopy (DHM) is a label-free imaging technique allowing visualization of transparent cells with classical imaging cell culture plates. The quantitative DHM phase contrast image provided is related both to the intracellular refractive index and to cell thickness. DHM is able to distinguish cellular morphological changes on two representative cell lines (HeLa and H9c2) when treated with doxorubicin and chloroquine, two cytotoxic compounds yielding distinct phenotypes. We analyzed parameters linked to cell morphology and to the intracellular content in endpoint measurements and further investigated them with timelapse recording. The results obtained by DHM were compared with other optical label-free microscopy techniques, namely Phase Contrast, Differential Interference Contrast and Transport of Intensity Equation (reconstructed from three bright-field images). For comparative purposes, images were acquired in a common 96-well plate format on the different motorized microscopes. In contrast to the other microscopies assayed, images generated with DHM can be easily quantified using a simple automatized on-the-fly analysis method for discriminating the different phenotypes generated in each cell line. The DHM technology is suitable for the development of robust and unbiased image-based assays.

| S-EPMC3894694 | biostudies-literature

Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy.

Project description:Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these limitations. In this work, we developed a new methodology for rapid, label-free molecular phenotyping of biological fluids (e.g., blood) by exploiting the recent advances in fast and highly efficient multidimensional inverse Laplace decomposition technique. We demonstrated that using two-dimensional T1-T2 correlational spectroscopy on a single drop of blood (<5??L), a highly time- and patient-specific 'molecular fingerprint' can be obtained in minutes. Machine learning techniques were introduced to transform the NMR correlational map into user-friendly information for point-of-care disease diagnostic and monitoring. The clinical utilities of this technique were demonstrated through the direct analysis of human whole blood in various physiological (e.g., oxygenated/deoxygenated states) and pathological (e.g., blood oxidation, hemoglobinopathies) conditions.

| S-EPMC7522972 | biostudies-literature

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy.

Project description:Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

| S-EPMC10900511 | biostudies-literature

EpiGe: A cytosine methyl-genotyping PCR-based machine-learning tool for rapid classification of medulloblastoma

Project description:Molecular classification of medulloblastoma is critical for the correct treatment of this malignant paediatric brain tumour. The analysis of genome-wide DNA methylation patterns has profoundly improved diagnostic precision and classification of brain tumours. However, the implementation of DNA methylation microarrays in daily clinical practice can be time-consuming, costly and inaccessible for many centres worldwide. We aimed to develop a machine-learning decision support system for rapid and cost-effective prediction of medulloblastoma methylation class directly from quantitative PCR data.

2023-08-23 | GSE210723 | GEO

Planar Photonic Crystal Biosensor for Quantitative Label-Free Cell Attachment Microscopy.

Project description:In this study, a planar-surface photonic crystal (PC) biosensor for quantitative, kinetic, label-free imaging of cell-surface interactions is demonstrated. The planar biosensor surface eliminates external stimuli to the cells caused by substrate topography to more accurately reflect smooth surface environment encountered by many cell types in vitro. Here, a fabrication approach that combines nanoreplica molding and a horizontal dipping process is used to planarize the surface of the PC biosensor. The planar PC biosensor maintains a high detection sensitivity that enables the monitoring of live cell-substrate interactions with spatial resolution sufficient for observing intracellular attachment strength gradients and the extensions of filopodia from the cell body. The evolution of cell morphology during the attachment and spreading process of 3T3 fibroblast cells is compared between planar and grating-structured PC biosensors. The planar surface effectively eliminates the directionally biased cellular attachment behaviors that are observed on the grating-structured surface. This work represents an important step forward in the development of label-free techniques for observing cellular processes without unintended external environmental modulation.

| S-EPMC4750395 | 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