Biology Reference
In-Depth Information
BOX 25.3 Cleaning the mess
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computational approaches for obtaining cell type specific information from
heterogeneous tissues
For many measurements performed on tissue samples consist-
ing of more than one cell type, current experimental techniques
and cost limitations allow measurement of a few biological
species across many different cell types (e.g., as in flow or mass-
based cytometry) or many biological species across a hetero-
geneous tissue that is devoid of cell-type information (a single
isolated cell type being a specific subset of the latter). Ideally,
a researcher may desire to measure many biological species, as
they are affected by their tissue microenvironment but at a cell
type-specific resolution, and for all of the different cell types in
a tissue.
Statistical deconvolution techniques incorporate tissue
heterogeneity information to capture both cell type-specific
and system-wide information from the same samples. These
techniques are performed in silico after data generation and are
based on the idea of exploiting sample to sample variation. First
developed in yeast
[9,10]
, they have been enhanced and tested
for the analysis of human blood gene expression samples
[11,12]
, though the basic concept can be expanded to other
complex tissues
[13]
or measurement types. By tracking how
measured gene expression fluctuates between samples in
relation to cell-frequency changes, it is possible to accurately
estimate the average type-specific expression of each cell type.
This estimated cell type-specific expression may then be used
to identify cell type-specific expression differences between
groups of samples for each cell type present in the tissue above
a certain (empirically determined) frequency, or reconstruct
biological samples having removed the effects of one or more of
the cell types. Conversely, through tracking the transcript
abundance of combinations of cell-specific markers it is
possible to accurately estimate the frequency of each cell
subset. The sensitivity of cell type specific expression performed
in this manner is often orders of magnitude higher than that
obtained by analyzing heterogeneous tissue samples and
provides a cellular context for each of the detected differences
between case and control.
a Continuum of CD8 (
รพ
) T Cell Phenotypes.
Immunity
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[3] Ornatsky OI, Kinach R, Bandura DR, Lou X, Tanner SD,
Baranov VI, et al. Development of analytical methods for
multiplex bio-assay with inductively coupled plasma mass
spectrometry. J Anal At Spectrom 2008;23(4):463
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9.
[4] Bandura DR, Baranov VI, Ornatsky OI, Antonov A,
Kinach R, Lou X, et al. Mass cytometry: technique for real
time single cell multitarget immunoassay based on induc-
tively coupled plasma time-of-flight mass spectrometry. Anal
Chem 2009;81(16):6813
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22.
e
[5]
Flatz L, Roychoudhuri R, Honda M, Filali-Mouhim A,
Goulet JP, Kettaf N, et al. Single-cell gene-expression
profiling reveals qualitatively distinct CD8T cells elicited by
different gene-based vaccines. Proc Natl Acad Sci USA
2011;108(14):5724
9.
[6] Han Q, Bradshaw EM, Nilsson B, Hafler DA, Love JC.
Multidimensional analysis of the frequencies and rates of
cytokine secretion from single cells by quantitative micro-
engraving. Lab Chip 2010;10(11):1391
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400.
[7] Garcia D, Ghansah I, Leblanc J, Butte MJ. Counting cells
with a low-cost integrated microfluidics-waveguide sensor.
Biomicrofluidics 2012;6(1):14115
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[8]
Leblanc J, Mueller AJ, Prinz A, Butte MJ. Optical planar
waveguide for cell counting. Appl Phys Lett 2012;100(4):
43701
437015.
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[9]
Lu P, Nakorchevskiy A, Marcotte EM. Expression deconvo-
lution: a reinterpretation of DNA microarray data reveals
dynamic changes in cell populations. Proc Natl Acad Sci
USA 2003;100(18):10370
5.
e
[10]
Stuart RO, Wachsman W, Berry CC, Wang-Rodriguez J,
Wasserman L, Klacansky I, et al. In silico dissection of cell
type-associated patterns of gene expression in prostate
cancer. Proc Natl Acad Sci USA 2004;101(2):615
20.
[11] Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z,
Clark HF. Deconvolution of blood microarray data identifies
cellular activation patterns in systemic lupus erythematosus.
PLoS One 2009;4(7):e6098.
e
References
[1] Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO,
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immune and drug responses across a human hematopoietic
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Population-specific
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Cytometry by Time-of-Flight Shows Combinatorial Cytokine
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and Virus-Specific Cell Niches within
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7.
a finite of states and a set of rules executed temporally
defining neighborhood cell interactions. Models in this
class make many simplifying assumptions and usually
lack all but the rudimentary principles of the biological
processes being modeled. To develop large-scale models,
researchers usually specify a small number of cell types and
molecular entities and the set of rules that describe cell
and molecular interaction behavior. They then create
many replicate cellular entities, such that the total number
of cells may be very large, even though the actual number
of cells of the same cell type and state may be limited.
Cell-to-cell variation may be generated by external input
