  Deconvolution of cell mixtures in "bulk" transcriptomic samples from
homogenate human tissue is important for understanding the pathologies of
diseases. However, several experimental and computational challenges remain in
developing and implementing transcriptomics-based deconvolution approaches,
especially those using a single cell/nuclei RNA-seq reference atlas, which are
becoming rapidly available across many tissues. Notably, deconvolution
algorithms are frequently developed using samples from tissues with similar
cell sizes. However, brain tissue or immune cell populations have cell types
with substantially different cell sizes, total mRNA expression, and
transcriptional activity. When existing deconvolution approaches are applied to
these tissues, these systematic differences in cell sizes and transcriptomic
activity confound accurate cell proportion estimates and instead may quantify
total mRNA content. Furthermore, there is a lack of standard reference atlases
and computational approaches to facilitate integrative analyses, including not
only bulk and single cell/nuclei RNA-seq data, but also new data modalities
from spatial -omic or imaging approaches. New multi-assay datasets need to be
collected with orthogonal data types generated from the same tissue block and
the same individual, to serve as a "gold standard" for evaluating new and
existing deconvolution methods. Below, we discuss these key challenges and how
they can be addressed with the acquisition of new datasets and approaches to
analysis.
