scholarly journals Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 311 ◽  
Author(s):  
Iordania Constantinou ◽  
Michael Jendrusch ◽  
Théo Aspert ◽  
Frederik Görlitz ◽  
André Schulze ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Cell Imaging ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Microfluidic Devices ◽  
Complex Data ◽  
Shot Classification ◽  
Single Cell Imaging ◽  
Small Channels

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

scholarly journals Computational Methods for Single-Cell Imaging and Omics Data Integration

2022 ◽  
Vol 8 ◽  
Author(s):  
Ebony Rose Watson ◽  
Atefeh Taherian Fard ◽  
Jessica Cara Mar
Keyword(s):  
Single Cell ◽  
Biomedical Research ◽  
Cell Imaging ◽  
New Technologies ◽  
Single Cells ◽  
Tissue Level ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
Imaging Data ◽  
Single Cell Imaging

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

scholarly journals Fast and robust non-negative matrix factorization for single-cell experiments

2021 ◽  
Author(s):  
Zachary J. DeBruine ◽  
Karsten Melcher ◽  
Timothy J. Triche
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
R Package ◽  
Complex Data ◽  
Technological Advances ◽  
Value Decomposition ◽  
Non Negative Matrix Factorization

AbstractNon-negative matrix factorization (NMF) is an intuitively appealing method to extract additive combinations of measurements from noisy or complex data. NMF is applied broadly to text and image processing, time-series analysis, and genomics, where recent technological advances permit sequencing experiments to measure the representation of tens of thousands of features in millions of single cells. In these experiments, a count of zero for a given feature in a given cell may indicate either the absence of that feature or an insufficient read coverage to detect that feature (“dropout”). In contrast to spectral decompositions such as the Singular Value Decomposition (SVD), the strictly positive imputation of signal by NMF is an ideal fit for single-cell data with ambiguous zeroes. Nevertheless, most single-cell analysis pipelines apply SVD or Principal Component Analysis (PCA) on transformed counts because these implementations are fast while current NMF implementations are slow. To address this need, we present an accessible NMF implementation that is much faster than PCA and rivals the runtimes of state-of-the-art SVD. NMF models learned with our implementation from raw count matrices yield intuitive summaries of complex biological processes, capturing coordinated gene activity and enrichment of sample metadata. Our NMF implementation, available in the RcppML (Rcpp Machine Learning library) R package, improves upon current NMF implementations by introducing a scaling diagonal to enable convex L1 regularization for feature engineering, reproducible factor scalings, and symmetric factorizations. RcppML NMF easily handles sparse datasets with millions of samples, making NMF an attractive replacement for PCA in the analysis of single-cell experiments.

scholarly journals Single cell imaging reveals cisplatin regulating interactions between transcription (co)factors and DNA

2021 ◽  
Vol 12 (15) ◽  
pp. 5419-5429
Author(s):  
Yu Lin ◽  
Kui Wu ◽  
Feifei Jia ◽  
Ling Chen ◽  
Zhaoying Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Imaging ◽  
Single Cells ◽  
Microscopy Imaging ◽  
Single Cell Imaging ◽  
Imaging Strategy ◽  
Damaged Dna

A dual-modal microscopy imaging strategy was developed to investigate in situ the interactions between transcription (co)factors with cisplatin damaged DNA in single cells, showing that cisplatin lesions disrupted the interactions of Smad3 with DNA.

scholarly journals Exploring endocytic compartment morphology with systematic genetics and single cell image analysis

2019 ◽  
Author(s):  
Mojca Mattiazzi Usaj ◽  
Nil Sahin ◽  
Helena Friesen ◽  
Carles Pons ◽  
Matej Usaj ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Endocytic Pathway ◽  
Cellular Heterogeneity ◽  
Incomplete Penetrance ◽  
Coat Proteins ◽  
Mutant Phenotypes ◽  
Membrane Components

ABSTRACTEndocytosis is a conserved process that mediates the internalization of nutrients and plasma membrane components, including receptors, for sorting to endosomes and the vacuole (lysosome). We combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments: coat proteins, actin patches, late endosome, and vacuole. This unbiased approach identified 17 mutant phenotypes and ∼1600 genes whose perturbation affected at least one of the four compartments. Numerous mutants were associated with multiple phenotypes, indicating that morphological pleiotropy is often seen within the endocytic pathway. Morphological profiles based on the 17 aberrant phenotypes were highly correlated for functionally related genes, enabling prediction of gene function. Incomplete penetrance was prevalent, and single-cell analysis enabled exploration of the mechanisms underlying cellular heterogeneity, which include replicative age, organelle inheritance, and stress response.

Single Cell Imaging of Human Cologenic Carcinoma Cells by 3-Tesla MRI

2004 ◽  
Vol 176 (03) ◽  
Author(s):  
UKM Teichgräber ◽  
JG Pinkernelle ◽  
F Neumann ◽  
T Benter ◽  
H Bruhn ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Imaging ◽  
Carcinoma Cells ◽  
3 Tesla ◽  
Single Cell Imaging ◽  
3 Tesla Mri

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

Enabling drug discovery and development through single-cell imaging

2018 ◽  
Vol 14 (2) ◽  
pp. 115-125 ◽  
Author(s):  
Andrea K. Pomerantz ◽  
Farid Sari-Sarraf ◽  
Kerri J. Grove ◽  
Liliana Pedro ◽  
Patrick J. Rudewicz ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Single Cell ◽  
Cell Imaging ◽  
Drug Discovery And Development ◽  
Single Cell Imaging
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