Development of image analysis software for quantification of viable cells in microchips

Abstract

Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

Long-term single-cell passaging of human iPSC fully supports pluripotency and high-efficient trilineage differentiation capacity

“…11,15 This discrepancy may be explained by the fact that these findings were linked to aneuploidies found within the cells, as a strong correlation between the proportion of cell lines with abnormal karyotypes and population doubling has been previously reported in an extensive study with both ESC and iPSC. 38 In addition, our software (CellCountAnalyser) was able to measure cell confluence quicker and more accurately than other software available, 19,[39][40][41] giving us the ability to split cells in their logphase of growing.…”

Long-term single-cell passaging of human iPSC fully supports pluripotency and high-efficient trilineage differentiation capacity

“…[11,14] This discrepancy may be explained by the fact these findings were linked to aneuploidies found within the studies, as a strong correlation between the proportion of cell lines with abnormal karyotypes and population doubling has been previously reported in an extensive study with both ESC and iPSC. [37] Additionally, our software (CellCountAnalyser) was able to measure cell confluence quicker and more accurately than other software's available [16,[38][39][40], getting us the ability to split cells always in log-phase of growing.…”

Basis of Image Analysis for Evaluating Cell Biomaterial Interaction Using Brightfield Microscopy

“…The first step was discussed in the previous section. The second step sometimes depends on the inherent characteristics of the acquisition tool and needs to be prepared following some restrictions [Georg et al, 2018]. Medical practitioners and biologists make use of user-friendly software packages that were developed over time.…”

“…Another more challenging problem is the counting of the objects (cells, spheroids, nuclei) of the same class or of more than one class and then the analysis of their pa- rameters on one large image [Coelho et al, 2009;Lu, 2015]. The object detection problem is applied in different domains and different approaches of one stage detectors such as you look only once (YOLO) and single shot detectors (SSD) [Redmon et al, 2016;Georg et al, 2018].…”