Cellular heterogeneity: when do differences make a difference?

By Steven J. Altschuler, Lani F. Wu. Cell, 2010

1,148 citations recorded by the source corpus.

A central challenge of biology is to understand how individual cells process information and respond to perturbations. Much of our current knowledge is based on ensemble measurements. However, cell-to-cell differences are always present to some degree in any population of cells, and the ensemble behaviors of a population may not represent the behaviors of any individual cell. Here, we discuss examples of when heterogeneity cannot be ignored, and describe practical strategies for analyzing and interpreting cellular heterogeneity; while ensemble measurements may be too simplistic, capturing all variation among cells may be unnecessary. To develop accurate models of individual cell behavior–be they in the form of cartoons, words, or mathematics–it is essential to identify which cell-cell differences are important and which can be ignored.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5c76a6cdb6d73c6f81c9c818aa11adb94b293dfd

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Citations in the corpus

Data-analysis strategies for image-based cell profiling (2017, neutral): “There are several ways of clustering a data set.”
Applications in image-based profiling of perturbations. (2016, background): “Some of the most exciting computational developments for profiling preserve single cell data and thus take into account the increasingly well-appreciated heterogeneity of cultured cell populations [11,12,48].”
Intelligent Image-Activated Cell Sorting. (2018, positive): “To understand such cell-to-cell differences, new approaches are needed to rapidly search through and sort out cells with unique chemical and morphological features from large heterogeneous populations (Altschuler and Wu, 2010; Chattopadhyay et al., 2014), as conventional technologies have been limit”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “The key, inter-related challenges include (1) adapting methods to the exponentially increasing scale of single-cell data; (2) ensuring that the resulting classification is reproducible across experiments and platforms; (3) finding the proper granularity and detecting hierarchies of types and subtype”