Standard Guide for Using Fluorescence Microscopy to Quantify the Spread Area of Fixed Cells

Importancia y uso:

4.1 Under well-controlled conditions, the quantitative evaluation of morphological features of a cell population can be used to identify changes in cellular behavior or state. Cell morphology changes may be expected when, for example, there is a response to changes in cellular cytoskeleton organization (1),2 a response of cells to toxic compounds, changes in differentiation state, and changes in adhesion properties of cells to a substrate by either chemical or mechanical-induced extracellular matrix-based (ECM-based) signaling pathways (2, 3). Typically, populations of cells exhibit a range of morphologies even when the cells are genetically identical and are in a homogeneous environment (4). This biological variation in cell response is due to both cell-cycle variations and stochasticity in the cellular reactions that control adhesion and spreading in cells. By using cell-by-cell, microscopy-based measurements and appropriate statistical sampling procedures, the distribution of cell morphologies such as cell spreading area per cell can be measured. This distribution is highly characteristic of the culture and conditions being examined.

4.2 It is important to note that the use of this technique for cells on or in a 3D scaffold material can complicate the interpretation of the data. The topographic transforms of the cells on a 3D material may require full volumetric imaging and not just wide-field fluorescence imaging as described here.

4.3 The following are several examples of how this measurement can be used in a laboratory:

4.3.1 Quantify Cellular Response to a Biomaterial—The measurement of cell spread area can be used to characterize the response of cells to biomaterials. For example, spreading of most cell types is extremely sensitive to the stiffness of the culture substrate (5, 6). It is important to note that cell response to an ECM may be dependent on the preparation of the matrix. For example, the same ECM proteins prepared in a fibrillar or non-fibrillar surface coating can result in different morphology response.

4.3.2 Quality Control Metric for General Cell Culture Conditions—Cell spread area may be a useful metric for monitoring a change in cell culture conditions (that is, due to a serum component, pH, passage number, confluence, etc.). Cell morphology is often altered when cells are stressed and proceeding through cell-death related processes (that is, apotoposis).

4.3.3 Quality Control Metric for Biomaterial Fabrication—Cell spread area measurements may be useful for generating specifications for a biomaterial. These specifications may stipulate how a particular cell line responds to a fabricated biomaterial.

4.3.4 Quality Control Metric for Cell Line Integrity and Morphology Benchmarking—The morphology characteristic of a cell line grown under specified conditions should ideally be the same over time and in different laboratories. Thus, cell spread area measurements may be useful for validating that no significant changes in the cell cultures have occurred. This measurement provides a benchmark that is useful for establishing the current state of the cell culture and a metric that can be charted to increased confidence for within and between laboratory comparisons of cellular measurements (7).

4.3.5 High Quality Training Data for Cell Morphology Image Analysis by Machine Learning (ML) Algorithms—The high-contrast staining practices described in this guide are designed to increase contrast during image collection and reduce variability in automated object edge detection algorithms. This can facilitate the generation of large data sets that can be used to train pattern recognition ML algorithms for the detection of specific cellular and non-cellular features. For example, the fluorescent whole-cell morphology and nuclei images can be used to train a machine learning algorithm to identify cell and non-cell pixels in non-stained transmission phase microscopy images. Fluorescent imaging settings such as exposure time, filters, pixel binning, and focus need to be considered to ensure image collection is appropriate for an ML algorithm training data (8).

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F04.46

Referida por:

F2739-19, F3294-18R24, F3088-22, F2944-20, F3659-24

Volúmen:

13.02

Palabras clave:

automated microscopy; biomaterials; cell cultures; cell morphology; cell staining; cell therapy; cytotoxicity; differentiation; fluorescence microscopy; image analysis; machine learning; quality control; segmentation; stem cells; tissue engineering; training data;