Corpus Engine Towards a reproducible .science file.
← All .science files

pmid:38348045

Lung single-cell RNA profiling reveals response of pulmonary capillary to sepsis-induced acute lung injury.

Ruhao Yang · Ting Zheng · Hongyu Xiang · Menglin Liu · Ke Hu

Frontiers in immunology · 2024 doi:10.3389/fimmu.2024.1308915

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:38348045
kind
science-record
content_hash
sha256:14ced1f8921f1685fe32700a4a0df0aa2c6f9ee171f85c86de5c3b1ab82636ff
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T19:20:17Z
updated_at
2026-11-01T21:32:30Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Lung single-cell RNA profiling reveals response of pulmonary capillary to sepsis-induced acute lung injury.
doi
10.3389/fimmu.2024.1308915
pmid
38348045
authors array · 5 items
[0]
Ruhao Yang
[1]
Ting Zheng
[2]
Hongyu Xiang
[3]
Menglin Liu
[4]
Ke Hu
journal
Frontiers in immunology
year
2024
abstract
Background: Sepsis-induced acute lung injury (ALI) poses a significant threat to human health. Endothelial cells, especially pulmonary capillaries, are the primary barriers against sepsis in the lungs. Therefore, investigating endothelial cell function is essential to understand the pathophysiological processes of sepsis-induced ALI. Methods: We downloaded single-cell RNA-seq expression data from GEO with accession number GSE207651. The mice underwent cecal ligation and puncture (CLP) surgery, and lung tissue samples were collected at 0, 24, and 48 h. The cells were annotated using the CellMarker database and FindAllMarkers functions. GO enrichment analyses were performed using the Metascape software. Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the "FindMarkers" function. The R package AUCell was used to score individual cells for pathway activities. The Cellchat package was used to explore intracellular communication. Results: Granulocytes increased significantly as the duration of endotoxemia increased. However, the number of T cells, NK cells, and B cells declined. Pulmonary capillary cells were grouped into three sub-clusters. Capillary-3 cells were enriched in the sham group, but declined sharply in the CLP.24 group. Capillary-1 cells peaked in the CLP.24 group, while Capillary-2 cells were enriched in the CLP.48 group. Furthermore, we found that Cd74+ Capillary-3 cells mainly participated in immune interactions. Plat+ Capillary-1 and Clec1a+ Capillary-2 are involved in various physiological processes. Regarding cell-cell interactions, Plat+ Capillary-1 plays the most critical role in granulocyte adherence to capillaries during ALI. Cd74+ Capillary cells expressing high levels of major histocompatibility complex (MHC) and mainly interacted with Cd8a+ T cells in the sham group. Conclusion: Plat+ capillaries are involved in the innate immune response through their interaction with neutrophils via ICAM-1 adhesion during endotoxemia, while Cd74+ capillaries epxressed high level of MHC proteins play a role in adaptive immune response through their interaction with T cells. However, it remains unclear whether the function of Cd74+ capillaries leans towards immunity or tolerance, and further studies are needed to confirm this.
claims array · 2 items
[0] object · 6 fields
id
claim-method
statement
Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:38348045
locator
sections[0] "Abstract" paragraph 2
quote
Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function.
state
complete
[1] object · 6 fields
id
claim-data-access
statement
The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:38348045
locator
sections[22] "Data availability statement" paragraph 2
quote
The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
state
complete
gaps array · 0 items
experiment object · 8 fields
state
complete
steps array · 3 items
[0] object · 13 fields
id
read_published_estimate
order
1
name
Read the published estimate from the source
kind
other
state
complete
blocked_on
null
description
The claim, method and evidence span published with the source article.
parameters object · 0 fields
inputs array · 1 item
[0]
source:pmid:38348045
outputs array · 1 item
[0]
the published estimate and its evidence
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
id
obtain_source_data
order
2
name
Obtain the source data from the named repository
kind
data-preparation
state
complete
blocked_on
null
description
The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE207651
outputs array · 1 item
[0]
the source data with its repository provenance
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
id
run_analysis
order
3
name
Run the published analysis
kind
analysis
state
complete
blocked_on
null
description
Apply the paper's statistical method to the resolved series and compare the result with the published target.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE207651
outputs array · 1 item
[0]
the replicated result and its comparison
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 0 items
data object · 3 fields
state
partial
assets array · 2 items
[0] object · 15 fields
id
source:pmid:38348045
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.3389/fimmu.2024.1308915
sha256
sha256:4edb37cdc102fd05af54ee4b124cfc27710c9d62a2f0ac32a873d4b1420b2ecc
bytes
40916
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.3389/fimmu.2024.1308915
version
null
note
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
structure object · 4 fields
pmcid
PMC10859485
source
europe_pmc_fulltext
coverage
whole_body_without_references
text_hash_scope
rendered_section_body
[1] object · 15 fields
id
data:geo:GSE207651
role
raw
format
geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207651
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE207651
version
null
note
The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
access_statement_kind
data_availability_statement
sample_accessions array · 4 items
[0]
GSM6304387
[1]
GSM6304388
[2]
GSM6304389
[3]
GSM6304390
sample_count
4
group_sizes
null
group_sizes_basis
null
platform
19057
taxon
Mus musculus
title
Gene expression profile at single cell level of the lung tissue of the septic mice
resolution_checked_at
2026-11-01T19:09:46Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 2 items
[0]
asset:data:geo:GSE207651 not downloaded
[1]
asset:data:geo:GSE207651 licence not read
analysis object · 4 fields
state
partial
methods array · 3 items
[0] object · 10 fields
id
method-1
family
principal_component_analysis
specific
pca
label
Principal component analysis
dsl_operations array · 1 item
[0]
pca_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
wide_numeric_matrix
evidence object · 3 fields
score
0.8
band
high
quote
Principal component analysis
[1] object · 10 fields
id
method-2
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
GO enrichment
[2] object · 10 fields
id
method-3
family
null
specific
null
label
Gene set enrichment Analysis (GSEA), variation Analysis (GSVA), and FindMarkers
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
pca_fit
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 1 item
[0]
GSE207651
resolved_accessions array · 1 item
[0]
GSE207651
statements array · 1 item
[0]
The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
basis
The paper names GSE207651 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
pca_fit
assets array · 2 items
[0]
source:pmid:38348045
[1]
data:geo:GSE207651
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE207651
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:08:36Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
10925
completion_tokens
1703
cached_tokens
264
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:38348045
type
paper
label
pmid:38348045
[1] object · 3 fields
id
source:pmid:38348045
type
manuscript
label
PMID 38348045 full text
[2] object · 3 fields
id
data:geo:GSE207651
type
dataset
label
GEO GSE207651
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:38348045
type
paper
label
pmid:38348045
[1] object · 3 fields
id
source:pmid:38348045
type
manuscript
label
PMID 38348045 full text
[2] object · 3 fields
id
data:geo:GSE207651
type
dataset
label
GEO GSE207651
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-38348045
type
extraction
used array · 1 item
[0]
source:pmid:38348045
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE207651
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE207651
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:38348045
parent_hash
sha256:c377e8e360b0adb2610153af9793553d5bff317ec12e161ad7de0701acf86b89
fork_of
null
gaps array · 7 items
[0]
asset:data:geo:GSE207651 licence not read
[1]
asset:data:geo:GSE207651 not downloaded
[2]
execution.not_run
[3]
method.reader_named_unmapped
[4]
review.human_critique
[5]
review.independent_reader
[6]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
scrna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
unsupported
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 3 items
[0]
f sepsis-induced ALI. Methods: We downloaded single-cell RNA-seq expression data from GEO with accession numb
[1]
Material and methods Analysis of scRNA-seq data The single-cell library was prepared fo
[2]
Single Cell 3′ Reagent Kits v3.1. Processed scRNA-seq data were required in the form of raw unique
order_basis
paper_text_order:422
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
principal_component_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
pca_fit
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Principal component analysis
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38348045", "kind": "science-record", "revision": 2, "content_hash": "sha256:14ced1f8921f1685fe32700a4a0df0aa2c6f9ee171f85c86de5c3b1ab82636ff", "created_at": "2026-11-01T19:20:17Z", "updated_at": "2026-11-01T21:32:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lung single-cell RNA profiling reveals response of pulmonary capillary to sepsis-induced acute lung injury.", "doi": "10.3389/fimmu.2024.1308915", "pmid": "38348045", "authors": ["Ruhao Yang", "Ting Zheng", "Hongyu Xiang", "Menglin Liu", "Ke Hu"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Sepsis-induced acute lung injury (ALI) poses a significant threat to human health. Endothelial cells, especially pulmonary capillaries, are the primary barriers against sepsis in the lungs. Therefore, investigating endothelial cell function is essential to understand the pathophysiological processes of sepsis-induced ALI.\nMethods: We downloaded single-cell RNA-seq expression data from GEO with accession number GSE207651. The mice underwent cecal ligation and puncture (CLP) surgery, and lung tissue samples were collected at 0, 24, and 48 h. The cells were annotated using the CellMarker database and FindAllMarkers functions. GO enrichment analyses were performed using the Metascape software. Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the \"FindMarkers\" function. The R package AUCell was used to score individual cells for pathway activities. The Cellchat package was used to explore intracellular communication.\nResults: Granulocytes increased significantly as the duration of endotoxemia increased. However, the number of T cells, NK cells, and B cells declined. Pulmonary capillary cells were grouped into three sub-clusters. Capillary-3 cells were enriched in the sham group, but declined sharply in the CLP.24 group. Capillary-1 cells peaked in the CLP.24 group, while Capillary-2 cells were enriched in the CLP.48 group. Furthermore, we found that Cd74+ Capillary-3 cells mainly participated in immune interactions. Plat+ Capillary-1 and Clec1a+ Capillary-2 are involved in various physiological processes. Regarding cell-cell interactions, Plat+ Capillary-1 plays the most critical role in granulocyte adherence to capillaries during ALI. Cd74+ Capillary cells expressing high levels of major histocompatibility complex (MHC) and mainly interacted with Cd8a+ T cells in the sham group.\nConclusion: Plat+ capillaries are involved in the innate immune response through their interaction with neutrophils via ICAM-1 adhesion during endotoxemia, while Cd74+ capillaries epxressed high level of MHC proteins play a role in adaptive immune response through their interaction with T cells. However, it remains unclear whether the function of Cd74+ capillaries leans towards immunity or tolerance, and further studies are needed to confirm this.", "claims": [{"id": "claim-method", "statement": "Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38348045", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38348045", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38348045"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE207651"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE207651"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38348045", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1308915", "sha256": "sha256:4edb37cdc102fd05af54ee4b124cfc27710c9d62a2f0ac32a873d4b1420b2ecc", "bytes": 40916, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1308915", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10859485", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE207651", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE207651", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE207651", "version": null, "note": "The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM6304387", "GSM6304388", "GSM6304389", "GSM6304390"], "sample_count": 4, "group_sizes": null, "group_sizes_basis": null, "platform": "19057", "taxon": "Mus musculus", "title": "Gene expression profile at single cell level of the lung tissue of the septic mice", "resolution_checked_at": "2026-11-01T19:09:46Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE207651 not downloaded", "asset:data:geo:GSE207651 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Principal component analysis"}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "GO enrichment"}}, {"id": "method-3", "family": null, "specific": null, "label": "Gene set enrichment Analysis (GSEA), variation Analysis (GSVA), and FindMarkers", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Gene set enrichment Analysis (GSEA) and variation Analysis (GSVA) were performed to identify differential signaling pathways. Differential expression genes were collected with the “FindMarkers” function."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE207651"], "resolved_accessions": ["GSE207651"], "statements": ["The code used to analyze the data in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE207651 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["pca_fit"], "assets": ["source:pmid:38348045", "data:geo:GSE207651"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE207651"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:08:36Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10925, "completion_tokens": 1703, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38348045", "type": "paper", "label": "pmid:38348045"}, {"id": "source:pmid:38348045", "type": "manuscript", "label": "PMID 38348045 full text"}, {"id": "data:geo:GSE207651", "type": "dataset", "label": "GEO GSE207651"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38348045", "type": "paper", "label": "pmid:38348045"}, {"id": "source:pmid:38348045", "type": "manuscript", "label": "PMID 38348045 full text"}, {"id": "data:geo:GSE207651", "type": "dataset", "label": "GEO GSE207651"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38348045", "type": "extraction", "used": ["source:pmid:38348045"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE207651", "type": "accession_resolution", "used": ["data:geo:GSE207651"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38348045", "parent_hash": "sha256:c377e8e360b0adb2610153af9793553d5bff317ec12e161ad7de0701acf86b89", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE207651 licence not read", "asset:data:geo:GSE207651 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["f sepsis-induced ALI. Methods: We downloaded single-cell RNA-seq expression data from GEO with accession numb", "Material and methods Analysis of scRNA-seq data The single-cell library was prepared fo", "Single Cell 3′ Reagent Kits v3.1. Processed scRNA-seq data were required in the form of raw unique"], "order_basis": "paper_text_order:422", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Principal component analysis"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}