scholarly journals In silico-labeled ghost cytometry

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Masashi Ugawa ◽  
Yoko Kawamura ◽  
Keisuke Toda ◽  
Kazuki Teranishi ◽  
Hikari Morita ◽  
...  

Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive ‘imaging’ data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.

2021 ◽  
Vol 9 ◽  
Author(s):  
Cindy X. Chen ◽  
Han Sang Park ◽  
Hillel Price ◽  
Adam Wax

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4704-4704
Author(s):  
Michael C Gundry ◽  
Lorenzo Brunetti ◽  
Dimitrios Laurin Wagner ◽  
Joanne Hsu ◽  
Mireya Paulina Velasquez ◽  
...  

Abstract Although CRISPR/Cas9 is now accessible to a wide variety of cell-types and model systems, efficient editing of hematopoietic cells remains challenging. We have designed and optimized a protocol for rapid and efficient delivery of CRISPR/Cas9 to hematopoietic cell lines and primary cells. Combining electroporation's high transfection efficiency and the reduced cellular toxicity of Cas9 protein versus plasmid in suspension cells, we are able to produce highly efficient gene disruption and knock-in in a variety of human cell types, including acute myeloid leukemia (AML) cell lines, B-acute lymphoid leukemia (ALL) cell lines, primary T-lymphocytes and primary hematopoietic stem/progenitor cells (HSPCs). Our protocol involves rapid sgRNA template design and PCR amplification, followed by overnight in-vitro transcription, sgRNA purification and sgRNA-Cas9 ribonucleoprotein (RNP) formation. We began by testing the protocol on three AML cell lines, in which we observed up to 98% knock-out (KO) of the ubiquitous hematopoietic marker CD45 (%CD45neg cells by flow cytometry: HL-60 - 98%, OCI-AML2 - 92%, Kasumi - 87%). Using multiple guides, we also induced KO of two B-cell markers (CD19 and CD22) in three B-cell cancer cell lines (BV173, Daudi and Nalm-6). In these three cell lines, up to 70% of cells displayed combined loss of both cell surface receptors, indicating disruption of all four alleles (%CD19negCD22neg cells by flow cytometry: BV-173 - 58%, Nalm-6 - 70%, Daudi - 18%). We then optimized our editing strategy in human primary cells. We observed highly efficient CD45 loss (86±2%; n=3) in activated T-cells by flow cytometry and confirmed this KO frequency using high-throughput sequencing. We next measured CD45 gene disruption in CD34+ HSPC cells isolated from cord blood and found that our system had 75±10% editing efficiency (n=4). Importantly, a 48-hour period of cytokine stimulation with SCF/TPO/FLT3L prior to electroporation was required for efficient gene knockout (0hr: 8±4%, 24hr: 41±12%, 48hr: 73±16%; p0vs24=0.0002, p24vs48=0.003; n=8). Our protocol induced efficient gene disruption of several relevant targets in CD34+ cells including DNMT3A ex7 (69±4%; n=5), DNMT3A ex10 (86±14%; n=10) and NR3C1 (75±6%; n=5), and near complete loss of protein by western blot. To verify that the edited CD34+ HSPCs cells maintained engraftment and multilineage differentiation capacity, we transplanted Cas9 only (n=8) and Cas9/hCD45-sg1 RNP edited cells (n=8) into sub-lethally irradiated NOD scid gamma (NSG) mice. To avoid possible donor-dependent bias, each experimental pair (i.e. one Cas9 only replicate and one Cas9/hCD45-sg1 RNP treated replicate) was performed on cells derived from a single cord blood. Human cells successfully engrafted in the bone marrow of 16/16 recipients and spleens of 13/16 recipients. Importantly, we observed significant levels of engraftment by hCD45neg cells in the bone marrow of 7/8 mice and in the spleen of 5/8 mice transplanted with Cas9/hCD45-sg1 RNP edited cells (Figure 1A; Figure 1B shows one representative pair). High-throughput sequencing confirmed that engrafted human cells in BM displayed hCD45indel frequencies consistent with the flow cytometry data. Finally, we considered whether these editing strategies could be used to introduce specific point mutations into primary human HSPCs using Cas9-mediated homology directed repair (HDR). Single-stranded oligonucleotide HDR templates (ssODNs) with 90bp homology arms to the human CD45 locus were designed to introduce three basepair changes, two of which result in the generation of a BsiWI site near the CD45-sg1 cut site. High-throughput sequencing of treated human HSPC samples revealed efficient precise knock-in (22±4%; n=4) of the mutant allele. In conclusion, we describe a fast and efficient protocol for both gene disruption and targeted gene editing of human hematopoietic cells, including HSPCs, using the CRISPR/Cas9 system. The ability to quickly and efficiently edit primary human HSPCs using HDR makes it possible to introduce or repair genetic variants identified in association with hematologic diseases such as leukemia or bone marrow failure. Moreover, the high efficiency of this system offers the possibility to perform large-scale combinatorial gene editing in HSPCs to model complex mutational landscapes. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Author(s):  
Andrea J. Radtke ◽  
Evelyn Kandov ◽  
Bradley Lowekamp ◽  
Emily Speranza ◽  
Colin J. Chu ◽  
...  

AbstractThe diverse composition of mammalian tissues poses challenges for understanding the cell-cell interactions required for organ homeostasis and how spatial relationships are perturbed during disease. Existing methods such as single-cell genomics, lacking a spatial context, and traditional immunofluorescence, capturing only 2-6 molecular features, cannot resolve these issues. Imaging technologies have been developed to address these problems, but each possesses limitations that constrain widespread use. Here we report a new method that overcomes major impediments to highly multi-plex tissue imaging. Iterative Bleaching Extends multi-pleXity (IBEX) uses an iterative staining and chemical bleaching method to enable high resolution imaging of >65 parameters in the same tissue section without physical degradation. IBEX can be employed with various types of conventional microscopes and permits use of both commercially available and user-generated antibodies in an ‘open’ system to allow easy adjustment of staining panels based on ongoing marker discovery efforts. We show how IBEX can also be used with amplified staining methods for imaging strongly fixed tissues with limited epitope retention and with oligonucleotide-based staining, allowing potential cross-referencing between flow cytometry, Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq), and IBEX analysis of the same tissue. To facilitate data processing, we provide an open source platform for automated registration of iterative images. IBEX thus represents a technology that can be rapidly integrated into most current laboratory workflows to achieve high content imaging to reveal the complex cellular landscape of diverse organs and tissues.Significance StatementSingle cell flow cytometry and genomic methods are rapidly increasing our knowledge of the diversity of cell types in metazoan tissues. However, suitably robust methods for placing these cells in a spatial context that reveal how their localization and putative interactions contribute to tissue physiology and pathology are still lacking. Here we provide a readily accessible pipeline (IBEX) for highly multi-plex immunofluorescent imaging that enables a fine-grained analysis of cells in their tissue context. Additionally, we describe extensions of the IBEX workflow to handle hard to image tissue preparations and a method to facilitate direct integration of the imaging data with flow cytometry and sequencing technologies.


2021 ◽  
Vol 118 (12) ◽  
pp. 123701
Author(s):  
Julie Martin-Wortham ◽  
Steffen M. Recktenwald ◽  
Marcelle G. M. Lopes ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
...  

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 749
Author(s):  
Julia Butt ◽  
Rajagopal Murugan ◽  
Theresa Hippchen ◽  
Sylvia Olberg ◽  
Monique van Straaten ◽  
...  

The emerging SARS-CoV-2 pandemic entails an urgent need for specific and sensitive high-throughput serological assays to assess SARS-CoV-2 epidemiology. We, therefore, aimed at developing a fluorescent-bead based SARS-CoV-2 multiplex serology assay for detection of antibody responses to the SARS-CoV-2 proteome. Proteins of the SARS-CoV-2 proteome and protein N of SARS-CoV-1 and common cold Coronaviruses (ccCoVs) were recombinantly expressed in E. coli or HEK293 cells. Assay performance was assessed in a COVID-19 case cohort (n = 48 hospitalized patients from Heidelberg) as well as n = 85 age- and sex-matched pre-pandemic controls from the ESTHER study. Assay validation included comparison with home-made immunofluorescence and commercial enzyme-linked immunosorbent (ELISA) assays. A sensitivity of 100% (95% CI: 86–100%) was achieved in COVID-19 patients 14 days post symptom onset with dual sero-positivity to SARS-CoV-2 N and the receptor-binding domain of the spike protein. The specificity obtained with this algorithm was 100% (95% CI: 96–100%). Antibody responses to ccCoVs N were abundantly high and did not correlate with those to SARS-CoV-2 N. Inclusion of additional SARS-CoV-2 proteins as well as separate assessment of immunoglobulin (Ig) classes M, A, and G allowed for explorative analyses regarding disease progression and course of antibody response. This newly developed SARS-CoV-2 multiplex serology assay achieved high sensitivity and specificity to determine SARS-CoV-2 sero-positivity. Its high throughput ability allows epidemiologic SARS-CoV-2 research in large population-based studies. Inclusion of additional pathogens into the panel as well as separate assessment of Ig isotypes will furthermore allow addressing research questions beyond SARS-CoV-2 sero-prevalence.


Cell Reports ◽  
2021 ◽  
Vol 34 (10) ◽  
pp. 108824
Author(s):  
Gregor Holzner ◽  
Bogdan Mateescu ◽  
Daniel van Leeuwen ◽  
Gea Cereghetti ◽  
Reinhard Dechant ◽  
...  

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.


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