GEN-IsoPlexis_RT_Nov22_Single-CellAdvances - 5

ROUND T ABLE
release that are often masked at the bulk level due to the
functional heterogeneity of T cell products. Using single-cell
functional proteomics to screen functional improvement strategies
also has the advantage to limit the use of animal experiments,
which not only are costly and time-consuming, but
also require lengthy applications to obtain ethical approval.
Peggy Sotiropoulou:
The immune system is one of the most complex systems
in the body. Immune-based therapies are based on very
complex biological interactions and processes. There are
dynamic interactions of individual cells that occur between
the infused engineered cells, the host immune cells, the
tumor cells, and the cells of the tumor microenvironment.
In adoptive T cell therapy, there is heterogeneity in clonal
properties. The product is polyclonal and contains different
cells with different identities and at different stages. So,
while a population level analysis is very useful to show the
potential of the T cell product as a whole, we can use singlecell
analysis to understand the diversity of each product.
After the identification of the specific cell populations that
have the potential to mediate clinical responses, we can
reengineer the cell product to create cell therapies that are
more efficacious and safe.
Abhishek Garg:
Single-cell resolution allows you to get deep insights
that are otherwise difficult to gain from structured or
controlled experiments. In our experience, single-cell
functional proteomics can help bridge a major technological
and interpretational gap creating " knowledge loss " when
the biomolecular analyses go from mRNA to protein (i.e.,
data generation & annotation) and finally to extrapolating
the functional context (i.e., data discovery). In our experience,
this knowledge loss is often fueled by limitations of
databanks, " data-lakes, " or databases on which multi-omics
data discovery strongly relies.
A good example of this originates from single-cell
transcriptomics (i.e., scRNAseq) data analyses. Standard
databases like Gene Ontology, Reactome, and KEGG are
often used on scRNAseq data to annotate and extrapolate
protein-level pathways or functions. However, a genetic
footprint of a pathway is not equivalent to its functional
footprint. A good example of this is the annotation of cytotoxicity
from scRNAseq data of T cells infiltrating a tumor.
Here, there is a tendency to frequently use cytotoxicity-related
genes (e.g., granzymes or perforins) to mark the presence of
antigen-specific T cell cytotoxicity potential, even though it
is well known in experimental literature that T cells that are
challenged with any inflammatory condition (not specifically a
tumor or tumor-specific antigen) will upregulate these mRNA,
just to keep them in the bank for future fast production when
they are actually needed for cytotoxic activity (e.g., when a
T cell actually encounters a cognate antigen).
But the difference between actual cytotoxicity vs. simply
expression of genes for eventual (future) cytotoxic usage
may not be that easy to anticipate just based on mRNA.
That only means there is the possibility to create the required
proteins, either immediately or in future. Thus, the difference
is the right context. A particularly good example of
how this can deceive the field is glioblastoma. Some T cells
infiltrating glioblastoma tumors do express genes coding for
these cytotoxic molecules, but if you take out these T cells
from the patients and you start testing them, you find that
they are not very functional. But nevertheless, because there
was detection of certain mRNA by transcriptomics (that
are tangentially but not reliably associated with functional
output), people theorized that immune checkpoint blocking
immunotherapy should work against glioblastoma.
In the last few years, all of the high-profile trials applying
immune checkpoint-blocking immunotherapies against
glioblastoma have failed and recently, when we started doing
single-cell functional proteomics, we saw this disconnect
more clearly than what was visible from mRNA analyses on
bulk tumor or single-cell level. Essentially, T cells from such
exceptionally stressful or hostile environments may have
some mRNA expression for some pathways but because they
are not primed correctly for the right antigens of that disease,
they may not be able to functionally exploit such mRNA
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