scholarly journals High-throughput Raman-activated cell sorting in the fingerprint region

2021 ◽  
Author(s):  
Matthew Lindley ◽  
Julia Gala de Pablo ◽  
Jorgen Walker Peterson ◽  
Akihiro Isozaki ◽  
Kotaro Hiramatsu ◽  
...  
Keyword(s):  
Real Time ◽  
Information Content ◽  
High Throughput ◽  
Cell Sorting ◽  
Biological Research ◽  
Cell Populations ◽  
Label Free ◽  
Trade Off ◽  
Chemical Content ◽  
Fingerprint Region

Cell sorting is the workhorse of biological research and medicine. Cell sorters are commonly used to sort heterogeneous cell populations based on their intrinsic features. Raman-activated cell sorting (RACS) has recently received considerable interest by virtue of its ability to discriminate cells by their intracellular chemical content, in a label-free manner. However, broad deployment of RACS beyond lab-based demonstrations is hindered by a fundamental trade-off between throughput and measurement bandwidth (i.e., cellular information content). Here we overcome this trade-off and demonstrate broadband RACS in the fingerprint region (300 - 1,600 cm-1) with a record high throughput of ~50 cells per second. This represents a 100x throughput increase compared to previous demonstrations of broadband fingerprint-region RACS. To show the utility of our RACS, we demonstrate real-time label-free sorting of microalgal cells based on their accumulation of carotenoids and polysaccharide granules. These results hold promise for medical, biofuel, and bioplastic applications.

PS02.046: UNRAVELING TUMOR HETEROGENEITY OF ESOPHAGEAL ADENOCARCINOMA (EAC) THROUGH HIGH-THROUGHPUT OF SORTED TUMOR CELL POPULATIONS

2018 ◽  
Vol 31 (Supplement_1) ◽  
pp. 133-133
Author(s):  
Elena Bonora ◽  
Federica Isidori ◽  
Isotta Bozzarelli ◽  
Marialuisa Lugaresi ◽  
Deborah Malvi ◽  
...  
Keyword(s):  
Tumor Cell ◽  
High Throughput ◽  
Cell Sorting ◽  
Tumor Heterogeneity ◽  
Conflicts Of Interest ◽  
New Technology ◽  
Cell Populations ◽  
Specific Cell ◽  
Mutational Status ◽  
Genetic Patterns

Abstract Background In EAC clinical, histological, immune-histochemical and genetic patterns were documented, which support the existence of biologically different sub populations. We studied genetic intra/inter tumor heterogeneity of EAC with a new technology based on sorting of specific cell populations. Methods Formalin embedded material obtained from 16 EAC surgical specimens, classified according to the presence/absence of intestinal metaplasia in esophagus (BIM) and stomach (GIM) in BIM + GIM- (Barret's like), BIM-GIM- (cardio-pyloric like), BIM—GIM + (gastric like) types, was processed using a high-throughput cell sorting technology. Stromal and tumor cell populations were sorted based on antibodies against vimentin/pan-cytokeratin and on DNA content. Targeted resequencing on DNA extracted from the sorted cells was performed for 63 cancer-related genes (OncoSeek panel). Results In 11/16 (68.75%) of cases a mutation in TP53 was detected, and in 2 we observed CDKN2A (TP53 regulator) mutations. In Barret's like and Gastric like sub types only mutations of TP53 or TP53regulator genes were present. In pyloric like type TP53 and/or other different mutations of whom 2 in HNF1A were detected (Figure 1). Conclusion Selective sorting led to new patterns of EAC tumor mutational status and heterogeneity, among others somatic mutations in HNF1A not previously found. Parallel analysis of unsorted samples failed to detect these specific mutations. The cardio-pyloric like sub type contains mutational patterns different than those of Barret's and gastric like types. Further research on additional cases is necessary to confirm these findings. Disclosure All authors have declared no conflicts of interest.

scholarly journals Pulsed Thermal Method for Monitoring Cell Proliferation in Real-Time

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2440
Author(s):  
Seppe Bormans ◽  
Gilles Oudebrouckx ◽  
Patrick Vandormael ◽  
Thijs Vandenryt ◽  
Patrick Wagner ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Real Time ◽  
Industrial Applications ◽  
Biological Research ◽  
Thermal Method ◽  
Label Free ◽  
Induced Voltage ◽  
Sensor Interface ◽  
Foil Substrate ◽  
Number Of Cells

The study of cell proliferation is of great importance for medical and biological research, as well as for industrial applications. To render the proliferation process accurately over time, real-time cell proliferation assay methods are required. This work presents a novel real-time and label-free approach for monitoring cell proliferation by continuously measuring changes in thermal properties that occur at the sensor interface during the process. The sensor consists of a single planar resistive structure deposited on a thin foil substrate, integrated at the bottom of a cell culture reservoir. During measurement, the structure is excited with square wave current pulses. Meanwhile, the temperature-induced voltage change measured over the structure is used to derive variations in the number of cells at the interface. This principle is demonstrated first by performing cell sedimentation measurements to quantify the presence of cells at the sensor interface in the absence of cell growth. Later, cell proliferation experiments were performed, whereby parameters such as the available nutrient content and the cell starting concentration were modified. Results from these experiments show that the thermal-based sensor is able to accurately measure variations in the number of cells at the interface. Moreover, the influence of the modified parameters could be observed in the obtained proliferation curves. These findings highlight the potential for the presented thermal method to be incorporated in a standardized well plate format for high-throughput monitoring of cell proliferation.

scholarly journals Precise label-free quantitative proteomes in high-throughput by microLC and data-independent SWATH acquisition

2016 ◽  
Author(s):  
Jakob Vowinckel ◽  
Aleksej Zelezniak ◽  
Artur Kibler ◽  
Roland Bruderer ◽  
Michael Muelleder ◽  
...  
Keyword(s):  
High Throughput ◽  
Flow Rate ◽  
Quantitative Proteomics ◽  
High Sensitivity ◽  
Data Consistency ◽  
Biological Research ◽  
Label Free ◽  
Key Technology ◽  
Sample Series ◽  
Acquisition Strategies

AbstractWhile quantitative proteomics is a key technology in biological research, the routine industry and diagnostics application is so far still limited by a moderate throughput, data consistency and robustness. In part, the restrictions emerge in the proteomics dependency on nanolitre/minute flow rate chromatography that enables a high sensitivity, but is difficult to handle on large sample series, and on the stochastic nature in data-dependent acquisition strategies. We here establish and benchmark a label-free, quantitative proteomics platform that uses microlitre/minute flow rate chromatography in combination with data-independent SWATH acquisition. Being able to largely compensate for the loss of sensitivity by exploiting the analytical capacities of microflow chromatography, we show that microLC-SWATH-MS is able to precisely quantify up to 4000 proteins in an hour or less, enables the consistent processing of sample series in high-throughput, and gains quantification precisions comparable to targeted proteomic assays. MicroLC-SWATH-MS can hence routinely process hundreds to thousands of samples to systematically create precise, label free quantitative proteomes.

scholarly journals Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

2019 ◽  
Author(s):  
Ahmad Ahsan Nawaz ◽  
Marta Urbanska ◽  
Maik Herbig ◽  
Martin Nötzel ◽  
Martin Kräter ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Waves ◽  
Blood Cells ◽  
Surface Acoustic Waves ◽  
Microfluidic Channel ◽  
Cell Types ◽  
Biological Research ◽  
Label Free ◽  
Molecular Specificity ◽  
Deformability Cytometry

The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time- and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into label-free sorting and opens up new possibilities for basic biological research and clinical therapeutic applications.

scholarly journals Deep Learning based Cell Classification in Imaging Flow Cytometer

2021 ◽  
Vol 1 (2) ◽  
pp. 18-27
Author(s):  
Yi Gu ◽  
Aiguo Chen ◽  
Xin Zhang ◽  
Chao Fan ◽  
Kang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Throughput ◽  
Cell Sorting ◽  
Future Trend ◽  
Flow Cytometer ◽  
Learning Technologies ◽  
Cell Analysis ◽  
Cell Classification ◽  
Cell Image

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

scholarly journals Label-Free Detection of Neuronal Differentiation in Cell Populations Using High-Throughput Live-Cell Imaging of PC12 Cells

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e56690 ◽  
Author(s):  
Sebastian Weber ◽  
María L. Fernández-Cachón ◽  
Juliana M. Nascimento ◽  
Steffen Knauer ◽  
Barbara Offermann ◽  
...  
Keyword(s):  
Neuronal Differentiation ◽  
Pc12 Cells ◽  
High Throughput ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Live Cell ◽  
Cell Populations ◽  
Label Free ◽  
Label Free Detection
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