On comparing heterogeneity across biomarkers

Abstract

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell’s phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring co-staining biomarkers on the same cells; instead we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the “gold standard” of co-staining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

Single-cell states versus single-cell atlases — two classes of heterogeneity that differ in meaning and method

“…Acknowledging this limitation is powerful for RNA sequencing, because it enables lineage designations to be made with very few total reads per cell [21,22•]. Covariation in biomarkers [23] increases the sensitivity of cell-type discrimination even further. Sequencing of fewer than 9000 transcripts per cell was able to identify rare secretory subtypes among intestinal cells [16], which is remarkable considering that the average cell contains ~10 5 transcripts [24].…”

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

“…[40] However, the formulation we describe below promotes interpretability via the mapping of cells to specific biomarker patterns. In addition, we are making no assumptions about the Gaussianity or distribution of cellular biomarker intensity profiles.…”

“…[39] Yet another looked at multiplexed phenotypic associations, in contrast to[39] which looked at biomarker associations, but also neglected spatial information. [40] Our method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. In addition, most other methods report intratumor heterogeneity as a single score, thus potentially mapping two spatially different organizations of the TMEs incorrectly to the same score.…”

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

“…There is growing evidence that some heterogeneity is related to physiological and evolutionary adaptations to new challenges [10, 11]. A recent study suggests that heterogeneity can be decomposed into different groups of biomarkers that are consistent with known signaling pathways, implying a mechanistic basis for the cell-to-cell variation [12]. It has also been shown that patterns of signaling heterogeneity can distinguish cellular populations with different drug sensitivities [13, 14].…”

“…One approach has been to segment the population into discrete subpopulations using Gaussian mixture models or k-means clustering which effectively reduce the scale of the data [12, 36]. This approach can be effective when discrete subpopulations can be identified, but may not be effective in large scale projects where the changes in heterogeneity may be more subtle, or the heterogeneity may be more complex.…”