Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 29,313 posts. Searching is done in the search box in upper left corner. I blog on anything to do with stroke. DO NOT DO ANYTHING SUGGESTED HERE AS I AM NOT MEDICALLY TRAINED, YOUR DOCTOR IS, LISTEN TO THEM. BUT I BET THEY DON'T KNOW HOW TO GET YOU 100% RECOVERED. I DON'T EITHER BUT HAVE PLENTY OF QUESTIONS FOR YOUR DOCTOR TO ANSWER.
Changing stroke rehab and research worldwide now.Time is Brain!trillions and trillions of neuronsthatDIEeach day because there areNOeffective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.
What this blog is for:
My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.
Wednesday, April 12, 2023
Decoding the molecular crosstalk between grafted stem cells and the stroke-injured brain
Seems to have a different mechanism than the exosome one in this research.
Graft hNSCs and host transcriptomes simultaneously identified using TRAP-seq
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Host microenvironment modulates the graft secretome
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Interactome analyses between host/graft transcriptomes predict molecular crosstalk
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BMP6-noggin is a potential host-graft crosstalk pathway for stroke brain recovery
Summary
Stem
cell therapy shows promise for multiple disorders; however, the
molecular crosstalk between grafted cells and host tissue is largely
unknown. Here, we take a step toward addressing this question. Using
translating ribosome affinity purification (TRAP) with sequencing tools,
we simultaneously decode the transcriptomes of graft and host for human
neural stem cells (hNSCs) transplanted into the stroke-injured rat
brain. Employing pathway analysis tools, we investigate the interactions
between the two transcriptomes to predict molecular pathways linking
host and graft genes; as proof of concept, we predict host-secreted
factors that signal to the graft and the downstream molecular cascades
they trigger in the graft. We identify a potential host-graft crosstalk
pathway where BMP6 from the stroke-injured brain induces graft secretion
of noggin, a known brain repair factor. Decoding the molecular
interplay between graft and host is a critical step toward deciphering
the molecular mechanisms of stem cell action.
However, the molecular mechanisms of stem cell action remain largely
unknown. A key step toward decoding the molecular crosstalk between
graft and host is to identify the expression profiles of both the graft
and its host environment, as these are fundamental to predict signaling
cascades and downstream biological processes affected in both. A major
challenge is to distinguish factors expressed by the transplanted cells
from those of the surrounding host cells, particularly as the ratio of
graft to host cells is very low. We overcome this hurdle by using
translating ribosome affinity purification (TRAP) to enrich for graft
mRNA.
TRAP isolates cell-specific tagged ribosomes and their bound mRNA,
thereby specifically enriching for translating RNAs and providing a gene
expression profile closer to that of the protein expression profile.
Additionally, rapid mRNA stabilization during extraction minimizes gene
expression changes, providing an accurate readout of the in vivo graft transcriptome.
Here,
we establish the use of TRAP, in combination with RNA sequencing
(TRAP-seq) and bioinformatic tools, to separate and identify the
transcriptomes of graft and host in our model system of human neural
stem cells (hNSCs) grafted into the stroke-injured rat brain, which
promotes stroke recovery.
Using pathway tools, we investigate the interactions between the two
transcriptomes to predict the molecular crosstalk between host and
graft. This unbiased approach enables us to build a molecular picture of
factors that potentially signal between host and graft, and link these
signals with downstream molecular cascades. In doing so, we develop
testable hypotheses regarding the molecular interactions between graft
and host, a critical first step toward defining the molecular mechanisms
of stem cell action. As proof of concept, we focus on paracrine
signaling from the host to the grafted cells; through the interactions
of host Bmp6 with graft-expressed genes, we demonstrate the
validity of our approach to build testable hypotheses with which we can
start to decode the molecular interplay between graft and host.
Results
Combining TRAP-seq and bioinformatic tools to analyze graft and host transcriptomes
To
separate graft and host transcriptomes, we first performed TRAP-seq on
hNSCs transplanted in naive and stroke-injured rat brains to enrich for
graft mRNA (Figures 1A and S1A). TRAP is based on the isolation of cell type-specific tagged ribosomes and their bound translating mRNA.
We genetically engineered our hNSCs for TRAP using a lentiviral vector
to ubiquitously express a GFP-tagged ribosomal protein RPL10a in hNSCs (Figure S1B). We detected GFP in 60% hNSCs by flow cytometry (Figure S1C); nestin staining confirmed maintenance of stemness in GFP+ neurospheres (Figures 1B and S1D).
TRAP-compatible hNSCs retained efficacy; rats that received hNSCs
exhibited significant behavior recovery in the whisker-paw reflex test
at 7 days post-transplantation compared with vehicle group (Figure 1C).
Figure 1TRAP-seq facilitates separation and identification of graft and host transcriptomes
As
TRAP requires living cells, we confirmed graft survival at 7 days
post-transplantation in naive and stroke rat brains, with significantly
greater hNSC survival in stroke than naive tissue (Figures 1D
and 1E). We next performed TRAP on a separate cohort of rats using an
anti-GFP antibody to pull down tagged ribosomes from the grafted hNSCs;
both the immunoprecipitated fraction (i.e., the TRAP fraction), which is
enriched for hNSC transcripts, and the flow-through fraction (i.e., the
TRAP-negative), which predominantly contains the host rat transcripts,
were collected (Figure 1A). In the TRAP fraction, the amount of mRNA isolated was greater from hNSCs grafted in stroke versus naive brains (Table S1).
To determine the enrichment of graft mRNA in the TRAP fraction, we
compared housekeeping gene expression in TRAP and control non-TRAP
samples using human-specific probes (Figure S1E). The TRAP method isolated human mRNA from grafted hNSCs in naive and stroke brains with a similar efficiency (Figure S1F), reaching average enrichments of 101- and 21.5-fold for graft in naive and stroke brain, respectively (Figure 1F). Rat housekeeping genes were detected in all TRAP samples, as expected
(Figure S1G).
Therefore, to further separate graft and host transcriptomes, we used
RNA sequencing followed by RSEM (RNA-Seq by Expectation Maximization),
The mapping accuracy of RSEM to a human-rat pooled reference
transcriptome was tested using samples containing solely hNSC reads or
rat brain reads; hNSC reads had a 0.7% ± 0.96% mis-assignment rate, and
rat brain reads had a 3.2% ± 0.17% mis-assignment rate (mean ± SD),
confirming that RSEM can differentiate between human and rat genes.
Using this strategy, we found that the percentages of mapped reads
mapping to the human transcriptome were 46% ± 14.9% and 24% ± 15.1% for
grafts in naive and stroke brains, respectively (mean ± SD; Figure S1H).
This difference indicates greater contamination from host RNA in the
stroke TRAP samples. However, the extrapolated total number of human
reads in the stroke TRAP samples was 3.4 times higher than from naive
brains, consistent with greater graft survival in the stroke environment
(mean total human reads ± SEM: stroke: 3 × 1012 ± 1.5 × 1012; naive: 9 × 1011 ± 5 × 1011).
Next,
we assessed the gene expression profiles of grafted hNSCs and their
respective host tissues. Analysis of the TRAP-negative, i.e., the host
transcriptome, identified 5,396 genes differentially expressed between
stroke and naive brain environments (Figure S2A).
Principal-component analysis (PCA) of these differentially expressed
genes (DEGs) showed distinct clusters between the two groups (Figure S2B). Gene set enrichment analysis revealed that these genes are enriched in biological processes associated with stroke,
with Gene Ontology (GO) terms associated with the immune response being
the dominant category; other biological processes were related to cell
death, brain plasticity, and vasculogenesis (Figure 1G).
Analysis of the TRAP fractions, i.e., the graft transcriptome,
identified 11,008 graft genes, of which 460 genes were differentially
expressed between hNSCs grafted in the stroke-injured versus naive
brains (Figure S2A), with distinct clustering between the two groups observed by PCA (Figure S2B). These findings are consistent with previous reports showing that NSC gene expression is environment dependent.
Gene set enrichment analysis of the graft DEGs identified five
overarching biological processes: (1) cell differentiation, including
neurogenesis, gliogenesis, and stem cell division; (2) cell death:
intrinsic and extrinsic apoptosis and necrosis; (3) RNA splicing; (4)
synaptic plasticity; and (5) lipid metabolism, including cholesterol and
fatty acid metabolism (Figure 1H).
Most notably, GO terms relating to the immune response—the dominant
category identified in the host brains—are absent, implying successful
separation of graft and host transcriptomes. Enrichment of cell
death-related genes is consistent with our observation of greater graft
survival in the stroke-injured versus naive brain (Figures 1D and 1E). This biological difference is further supported by an increased pro-survival BCL2:BAX ratio (Figure 1I) and significant decreased expression of cell death-related genes in hNSCs grafted in the stroke versus naive environment (Figures 1J and 1K). In contrast, host expression of cell death-related genes increased in stroke versus naive brains (Figure 1J). The reciprocal regulation of the most prominent pro-death DEGs by the host and graft was confirmed by qPCR analysis (Figure 1K), except for the procaspase-8 gene Casp8;
caspase-8 may be regulated more at the post-translational level.
Together, immune response GO terms identified in host but not graft,
cell death-related genes regulated in opposite directions in graft and
host, and a predicted increase in graft survival in the stroke
environment, which we confirm, provide validation of successful
separation of the graft and host transcriptomes. Thus, by combining
TRAP-seq with RSEM, we can successfully distinguish the transcriptome of
the grafted hNSCs from that of the host rat brain and subsequently
identify graft biology affected by the host environment.
Predicting the molecular signals by which the host brain modulates the graft
For
proof of concept that interaction analyses of graft and host
transcriptomes can identify molecular signaling between the two
entities, we investigated how the host microenvironment modulates the
graft at the molecular level. We focused on the host secretome as
paracrine signaling by secreted factors is a major mechanism of cellular
communication. Of the host brain DEGs, 390 encoded for secreted
proteins. The majority of these secretome DEGs encoded for extracellular
matrix and other proteins (58.2%), followed by cytokines (10.5%),
peptidases (9.0%), growth factors (8.7%), enzymes (8.2%), transporters
(4.4%), phosphatases (0.5%), and kinases (0.5%; Figure 2A).
Figure 2Graft and host transcriptome interactome analyses predict signaling from the host brain to the graft
To
predict which host-secreted factors signal to the grafted hNSCs and to
identify the downstream graft genes that might be regulated by them, we
used IPA upstream regulator (UR) analysis
to identify differentially expressed host secretome genes that are URs of graft DEGs. We identified 15 such host brain genes (Figure 2B), which associated with 89 target graft DEGs. Thirteen of these graft genes, including NOG, BAX, and seven transcription factors, were targets of three or more host URs (Figure S3),
suggesting these could be key graft genes involved in converting host
signals into changes in graft biology. The molecular functions of the
host URs were enriched for terms relating to cell differentiation (e.g.,
differentiation of neurons and neuroglia), cell survival, and fatty
acid metabolism (Figure 2C).
Notably, these terms align with the key biological processes affected
in the graft, as determined by the graft transcriptome analysis (Figure 1H),
providing confidence in this analysis approach and highlighting how we
can begin to link changes in graft biology with host molecular signals
that could drive these biological changes. The hNSC graft expresses
receptors for 11 of the identified host URs (Table S2),
confirming the potential of these host factors to directly signal to
the graft. Furthermore, canonical pathway analysis of graft DEGs
identified signaling pathways in the graft activated by several host
URs, notably Bmp6, Igf1, Jag, Reln, and TGFβ1 (Figure 2D), further strengthening the hypothesis that these host-secreted factors signal to the graft.
To
validate our analysis approach, we investigated the effects of BMP6 on
hNSCs. BMP6 is a host brain factor predicted to be a UR of two graft
signaling pathways identified by our canonical pathway analysis (Figures 2D and 2E), namely “BMP signaling,” which is predicted to be the most significantly activated pathway identified (Z score: 2.14), and “STAT3 signaling,” which has a low activation score (Z score: 0.65), implying a lower probability of activation. Bmp6 encodes bone morphogenetic protein 6, and the grafted hNSC expresses five BMP receptor genes (Table S2). qPCR analyses confirmed increased Bmp6 expression in the stroke (versus naive) brain both in the presence (Figure 2F)
and absence of grafted hNSCs (relative quantitation ± SEM: naive 1.2 ±
0.3; stroke 22 ± 8.1; p < 0.05, n = 6), indicating that Bmp6 elevation is a brain injury response as previously reported.
The activity of these two signaling pathways in cultured hNSCs treated
with recombinant BMP6 was determined by immunoblotting analysis of
SMAD1/5/8 phosphorylation, the downstream signal transducer of the BMP
pathway,
and STAT3 phosphorylation (Figures 2E, 2G, and 2H). BMP6 increased phosphorylation of SMAD1/5/8, but not STAT3 (Figures 2G
and 2H), indicating activation of the canonical BMP signaling pathway,
but not the STAT3 pathway, consistent with our analysis predictions. To
further validate our analysis approach, we tested whether BMP6 acts as a
UR to other graft genes as predicted (Figure 2I). BMP6 stimulation of cultured hNSCs significantly increased expression of ID3 and showed a trend for decreased expression of E2F2 and ETV5 (Figure 2J). This mirrored the in vivo gene expression pattern observed for hNSCs grafted in stroke versus naive brains (Figure 2K), supporting the idea that BMP6 modulates these graft genes in vivo. FOSL1 gene expression changes were opposite in vitro versus in vivo (Figures 2J and 2K), suggesting that other host genes modulate graft FOSL1
expression in the stroke brain. We also confirmed that BMP6
significantly increased graft expression of NOG, at both the RNA and
protein level, as predicted (Figures 4); this is discussed in more detail later.
Confirmation of increased graft SMAD signaling in vivo was achieved by staining for pSMAD and the transcription factor ID3, a known downstream target of BMP6 and pSMAD
identified by our RNA-seq data (Figures 2I and 2J). Both pSMAD+ and ID3+ graft cells (Figures 2L and S4A–S4E)
were more prominent in the stroke environment than in the naive brain.
As the pSMAD signal was relatively weak, potentially due to the inherent
difficulties in detecting phospho-proteins, we quantified ID3+ graft
cells and found they were almost 7-fold more abundant in the stroke
versus naive brain (% ID3+ graft cells: stroke 8.8% ± 3.2%; naive 1.4% ±
0.6%; mean ± SEM; n = 3; Figure S4E).
These data agree with our analyses-based prediction of increased
BMP6/pSMAD signaling in the stroke graft. Notably, graft (and host)
cells were only pSMAD+ or ID3+ in areas of injury (i.e., stroke lesion
and border; top of the needle track), identifying a very
environment-specific effect; most graft ID3+ cells in the stroke brain
were at the lesion border (81% ± 8.7%: mean ± SEM; n = 3). O’Shea et al.
also observed this and suggested that ID3+ stroke border cells are
wound repair astrocytes. Graft cells were more responsive to the stroke
environment than host cells, with 13.8% ± 3% of graft cells in the
lesion border expressing ID3 versus 6.8% ± 2% of host cells (mean ± SEM;
n = 3). Host BMP6 levels were also highest at the lesion border,
expressed by astrocytes but not microglia (Figures S4F–S4H), consistent with our hypothesis that host BMP6, upregulated by injury, increases graft pSMAD signaling.
In
summary, by analyzing the interaction between the host secretome and
graft transcriptome, we start to elucidate the molecular communication
from host to graft (e.g., ligand-receptor interactions) and the
downstream genes and signaling pathways altered in the graft by these
host URs. In this way, we can begin to predict the response of the graft
to its environment.
Host microenvironment modulates graft secretome: Host and graft crosstalk mediated by BMP6-NOG interaction
As the graft is expected to exert its therapeutic effects via paracrine factors,
the effect of the host on the graft secretome is of interest, as the
host environment could modulate graft efficacy. Of the 371 secretome
genes expressed by hNSCs in the stroke-injured brain, 13 were
significantly upregulated by hNSCs in the stroke versus naive brain,
indicating a conditional response of these graft secretome genes to
their environment (Figure 3A).
Consistent with the potential for these graft factors to influence
graft efficacy, the functions of the 13 graft secretome genes are
enriched for biological processes associated with stroke recovery,
including plasticity (e.g., regeneration of axons and neurites),
vasculogenesis, blood-brain barrier function, and immune modulation
(Figure 3B). Notably, expression levels of two of these genes, NOG and MATN2, positively correlated with the extent of recovery at 1 week post-transplantation (Figure 3C),
suggesting that these factors could be important determinants of graft
efficacy. Indeed, NOG and MATN2 have been associated with enhanced
stroke recovery
To further validate our crosstalk analyses, we focused on NOG,
the most highly upregulated graft secretome gene in the stroke brain
environment (versus naive) that positively correlates with behavior
recovery. NOG encodes noggin, an antagonist of transforming growth factor-β (TGF-β) superfamily members,
We confirmed graft expression of NOG by qPCR (Figure 4A) and by in situ hybridization (ISH) (Figure 4B) using human-specific probes. UR analysis, to determine host paracrine factors that might modulate graft expression of NOG, identified three host-expressed genes (Bmp6, Igf1, and Tgfb1) associated with NOG (Figures 4C and S3) and upregulated in stroke tissue (Figure 4D).
BMP6 is predicted to activate NOG, IGF1 to inhibit NOG, and TGF-β-1 to
mirror NOG expression changes in response to various stimuli. To test if
BMP6 increases NOG expression, we added recombinant BMP6
(rBMP6) to cultured hNSCs and used recombinant TGF-β-1 as a UR control.
As predicted, only BMP6 significantly upregulated gene expression of NOG (Figure 4E). Furthermore, BMP6-induced noggin protein expression by hNSCs was confirmed by ELISA (Figure 4F). Notably, our RNA-seq data suggested a positive correlation between the expression of host Bmp6 and graft NOG (Figure 4G), which we confirmed in vitro by showing that rBMP6 increased hNSC NOG expression in a dose-dependent manner (Figure 4H).
Figure 4Host and graft crosstalk mediated by BMP-NOG interplay
These
data imply that the host microenvironment modulates the graft secretome
and thereby modulates graft efficacy. Taken together with our data
showing that BMP6 activates the hNSC BMP canonical signaling pathway (Figures 2G–2L), we predict the following molecular crosstalk between the host stroke brain and grafted hNSCs (Figure 4J):
BMP6 secreted by the stroke-injured brain activates the BMP signaling
pathway in the graft, increasing graft expression of NOG in a
dose-dependent manner. In turn, graft-secreted noggin acts on the host
brain, altering various biological processes (as indicated in Figure 3B)
that induce stroke recovery, in a dose-dependent manner. Thus, by
looking at the interaction between graft and host transcriptomes, we can
build hypotheses to start to decode the molecular crosstalk between
graft and host.
Discussion
Here,
we present an approach, applicable to many stem cell transplantation
paradigms, to predict the molecular crosstalk between grafted stem cells
and their host tissue. We used TRAP-seq with RSEM analysis to
simultaneously decode the transcriptomes of the graft and its host
tissue, followed by UR and canonical pathway analyses to predict the
molecular cascades linking graft and host genes. We applied this to our
model system of hNSCs transplanted into the stroke-injured brain and
identified a potential host-graft crosstalk pathway whereby BMP6 from
the stroke-injured brain induces canonical BMP signaling in the graft
and hNSC secretion of noggin, a factor known to promote stroke brain
recovery.
Having
both host and graft transcriptomes offers, for the first time, an
unbiased way to decode the molecular interactions between host and
graft. It enables us to build a picture of the molecular cascade of
events that occur when host and graft interact, from URs that signal
between the host and graft to downstream signaling pathways and
biological processes these URs might affect. In essence, we can generate
data-driven hypotheses with which to interrogate the complex molecular
interactions between graft and host. In our proof-of-concept study, we
focused on communication from the host to the graft, but the same
approach can be used to investigate communication from the graft to the
host to predict signals from the grafted stem cells, and the host
molecular cascades they activate, that drive recovery. This is a
critical step toward defining the molecular mechanisms of stem cell
action and host tissue recovery.
The important concept of the host environment modulating the graft is just starting to be addressed.
Here, we demonstrate that the environment greatly affects the
host-graft molecular crosstalk. The graft received different molecular
signals and responded differently (e.g., better graft survival,
different graft signaling pathways activated) in the stroke versus naive
brain environment. Notably, we observed stroke-dependent effects on
graft secretome gene expression (e.g., NOG). This is significant because the graft can exert therapeutic effects via paracrine signaling,
irrespective of whether the cells are in a progenitor or differentiated
state, implying that the host environment can affect graft efficacy by
modulating the graft secretome. Within the stroke brain, there are
different microenvironments depending on proximity to the lesion,
suggesting that different regions of the graft undergo different
molecular interactions with the host.
Indeed, we observed that graft (and host) cells very close to the
lesion responded with upregulated expression of the transcription factor
ID3, while those further away did not; this was also observed by O’Shea
et al.
This implies that different regions of the graft, in different
microenvironments, have unique molecular interactions with the host and
elicit distinct effects on the brain. Notably, not all graft cells close
to the lesion exhibited increased ID3 expression, potentially due to
different microenvironments around the lesion or due to graft
heterogeneity, with only certain cell types responding in this manner.
Taken together, the location and timing of cell transplantation and the
types of cells being transplanted will all affect the host-graft
molecular crosstalk and, ultimately, graft efficacy. Moreover, since the
host environment and the graft mature over time,
Deciphering
the molecular crosstalk between graft and host has significant
therapeutic implications. Identifying host factors that modulate graft
efficacy can help identify an optimal time window for cell
transplantation and provide potential biomarkers for selecting patients
that would be good responders to stem cell therapy. For example, in our
model system, host BMP6 is a potential biomarker as it modulates graft
expression of noggin, a secreted protein positively linked to stroke
recovery.
Patients expressing BMP6 may respond better to cell therapy than those
without because graft-secreted noggin, and thus graft efficacy, would be
enhanced in these patients. Furthermore, decoding the molecular
mechanism of stem cell action can lead to better therapeutic strategies:
key graft-secreted factors identified to promote recovery can be used
therapeutically instead of stem cells,
and identifying key host molecular pathways triggered by the stem cells can offer innovative drug targets for stroke recovery.
Limitations of the study
This
study describes an unbiased approach to identify the molecular
crosstalk between graft and host, a critical first step toward decoding
the molecular mechanisms of grafted stem cell action. However, by using
ribosome-bound RNA, this method does not address changes in microRNA or
post-translational protein modifications, which also affect biological
processes. Our bulk RNA-seq approach gives a broad picture of potential
graft-host molecular interactions, but spatial and single-cell
transcriptomics are needed to decipher the finer details of
neighborhood- and cell type-specific interactions. While our study
successfully predicts molecular pathways linking graft and host genes,
future investigations require blocking or knockdown studies to identify
which interactions are important for graft efficacy.
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