Electrical Characterization of Nanostructured p-Silicon Electrodes for Bioimpedance Measurements on Single Cell Level

Author(s):  
U. Pliquett ◽  
M. Westenthanner ◽  
M. Rommel ◽  
A. Bauer ◽  
D. Beckmann
2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Likhitha Kolla ◽  
Michael C. Kelly ◽  
Zoe F. Mann ◽  
Alejandro Anaya-Rocha ◽  
Kathryn Ellis ◽  
...  

Author(s):  
Wenhong Hou ◽  
Li Duan ◽  
Changyuan Huang ◽  
Xingfu Li ◽  
Xiao Xu ◽  
...  

Mesenchymal stem/stromal cells (MSCs) are promising cell sources for regenerative medicine and the treatment of autoimmune disorders. Comparing MSCs from different tissues at the single-cell level is fundamental for optimizing clinical applications. Here we analyzed single-cell RNA-seq data of MSCs from four tissues, namely umbilical cord, bone marrow, synovial tissue, and adipose tissue. We identified three major cell subpopulations, namely osteo-MSCs, chondro-MSCs, and adipo/myo-MSCs, across all MSC samples. MSCs from the umbilical cord exhibited the highest immunosuppression, potentially indicating it is the best immune modulator for autoimmune diseases. MSC subpopulations, with different subtypes and tissue sources, showed pronounced differences in differentiation potentials. After we compared the cell subpopulations and cell status pre-and-post chondrogenesis induction, osteogenesis induction, and adipogenesis induction, respectively, we found MSC subpopulations expanded and differentiated when their subtypes consist with induction directions, while the other subpopulations shrank. We identified the genes and transcription factors underlying each induction at the single-cell level and subpopulation level, providing better targets for improving induction efficiency.


2019 ◽  
Vol 14 (7) ◽  
pp. 1800675 ◽  
Author(s):  
Eva Pekle ◽  
Andrew Smith ◽  
Guglielmo Rosignoli ◽  
Christopher Sellick ◽  
C. M. Smales ◽  
...  

The Analyst ◽  
2019 ◽  
Vol 144 (3) ◽  
pp. 943-953 ◽  
Author(s):  
Ruben Weiss ◽  
Márton Palatinszky ◽  
Michael Wagner ◽  
Reinhard Niessner ◽  
Martin Elsner ◽  
...  

Detection and characterization of microorganisms is essential for both clinical diagnostics and environmental studies.


2021 ◽  
Author(s):  
Jan Dohmen ◽  
Artem Baranovskii ◽  
Bora Uyar ◽  
Jonathan Ronen ◽  
Vedran Franke ◽  
...  

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.


2016 ◽  
Vol 89 (9) ◽  
pp. 826-834 ◽  
Author(s):  
Zsuzsanna Nerada ◽  
Zoltán Hegyi ◽  
Áron Szepesi ◽  
Szilárd Tóth ◽  
Csilla Hegedüs ◽  
...  

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