Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression — TEI-tagged view

Étienne Becht, Nicolás A. Giraldo, Laetitia Lacroix, Bénédicte Buttard, Nabila Elarouci, Florent Petitprez, Janick Sèlves, Pierre Laurent‐Puig, Catherine Sautès‐Fridman, Wolf H. Fridman, Aurélien de Reyniès · 2016 · genomics · doi:10.1186/s13059-016-1070-5 · TEI xml · tagged edition · index · concepts · authors
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MCP-counter transcriptomic markers cell population negative controls2 arbitrary units CIBERSORT2 flow cytometry2 limit of detection immunohistochemical system-or-tool-building173
Method, as described: markers selected on three simultaneous thresholds against 1194 purified microenvironment samples and 742 tumour cell lines as negative controls, scored as the log2 mean of their expression, validated on 4804 further samples, on latin-square RNA mixtures and on 38 immunohistochemically quantified tumours

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenona bulk transcriptome averaging signals from every cell type present, so that the composition of the tissue is recoverable only through markers that belong to one population and nothing else— “MCP-counter is based on the methodological framework of transcriptomic markers (TM), defined as gene expression features expressed in one and only one cell population and whose expression shows little variation within the population of interest (step 1). It directly follows that, within a sample composed of many different cell populations…
claim-typemethod— “We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support…
method — evidence— “These transcriptomes were normalized to allow their integration into a large meta-dataset which includes 1194 immune or non-immune microenvironment cell population samples; 742 tumor cell line samples were included as negative controls (Additional file 1: Table S1). No hematopoietic tumor cell line was included as a negative control as the…
object of study — evidence— “We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support…
research question — evidence— “MCP-counter is based on the methodological framework of transcriptomic markers (TM), defined as gene expression features expressed in one and only one cell population and whose expression shows little variation within the population of interest (step 1). It directly follows that, within a sample composed of many different cell populations…
contribution — evidence— “We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support…
transferfrom experimental design: the latin square, carried from agricultural experimental design into the layout of an RNA mixture so that no two populations' concentrations are collinear— “For this purpose, we designed an in vitro RNA mixture experiment (Fig. 3a). Immune populations were purified from healthy donors’ peripheral blood; their RNAs were extracted and mixed in highly variable concentrations (from 0.7 to 46 % of the sample’s RNA). The RNA proportions of the populations were arranged in two transposed latin squares…transfers[0]
transferfrom signal detection theory: the ROC curve and the signal-to-noise ratio, carried from signal detection into the selection criteria for a gene marker— “Three criteria were then calculated for each feature (probe set) within the discovery set: a) the mean log2-expression difference between positive and negative samples (a threshold of 2 was applied); b) the area under the ROC curve (AUC) of the feature for the identification of the positive samples (threshold of 0.97); and c) a measure of the…transfers[1]
mechanisma marker that belongs to one population and nothing else if a gene is expressed in exactly one cell type and at a stable level within it, then its expression in a mixture is proportional to how much of that cell type is there - no unmixing, no reference matrix, no dependence on what else is in the sample. The whole method is that one property, and the paper says such genes are not guaranteed to exist— “MCP-counter is based on the methodological framework of transcriptomic markers (TM), defined as gene expression features expressed in one and only one cell population and whose expression shows little variation within the population of interest (step 1). It directly follows that, within a sample composed of many different cell populations…mechanisms[0]
mechanismthe controls decide the vocabulary a marker qualifies by separating positive from negative samples, so a population with no purified counter-example in a public repository cannot be given markers at all. Effector memory CD4 T cells are named as a casualty: they would need central memory CD4 and effector memory CD8 controls, and those were not available— “We thus discarded those for which appropriate negative controls were not publically available (for instance, identifying TM for effector memory CD4 T cells at least requires negative controls such as central memory CD4 T cells and effector memory CD8 T cells), those with few positive samples, or those with no identified markers after the…mechanisms[1]
mechanismbreadth of controls costing specificity 21 cancer types among the negative controls make the markers portable across tissues and remove any marker that any of those tissues expresses. NCAM1/CD56, a standard NK marker, is discarded because nervous malignant cells carry it - which protects every other tissue and gives up NK quantification in brain— “This large diversity of control samples may, however, discard TM which would be relevant in a specific setting: for instance, the screening procedure discarded NCAM1 (CD56), a widely used marker of NK cells, as it is also expressed by nervous malignant cells and is thus unsuitable to quantify NK cells in brain samples.”mechanisms[2]
mechanisma scale with no zero and no unit the score is a log2 mean of marker expression, so it moves with abundance and has no absolute meaning. Two consequences the paper states: the units depend on the measurement platform, and mapping them onto percentages would require calibrating samples of known composition - which is the step aran2017 later takes— “To complement the current multi-sample use of MCP-counter, designed for exploratory analyses, one could notably settle on a desired gene expression platform and use a set of calibrating samples. For instance, the in vitro RNA mixtures described here could help to map MCP-counter abundance scores to non-arbitrary units, such as the percentage…mechanisms[3]
critiques methods that characterise proportions of immune populations within the leukocyte fraction while omitting the overall abundance of leukocytes in the sample— “Different transcriptome-based computational methods were recently proposed to characterize the proportions of immune populations within leukocytes but omit the critical parameter of the overall abundance of leukocytes in the sample. Other published methods lack quantitative validation or are limited to only two cell populations.”claim_relations[0]
qualifies CIBERSORT - not as wrong but as answering a different question; the paper's own simulation has it estimating stable within-leukocyte proportions correctly while total abundance changes— “To illustrate these differences, we simulated mRNA mixtures where the within-leukocyte proportion of five immune cell populations was kept constant and equal while the proportions of tumor cells varied (Fig. 4a). In this setting, only MCP-counter was able to accurately reflect the difference in immune cell abundances across simulated…claim_relations[1]
qualifies its own headline - the abstract says absolute abundance and the discussion says arbitrary, platform-dependent units comparable only within a platform— “MCP-counter scores linearly correlate with the corresponding cell population abundances across samples, but they are expressed in arbitrary units. These arbitrary units are dependent on the gene expression platform used to produce the data and one can only compare samples produced with the same gene expression platforms.”claim_relations[2]
extends immunohistochemistry, which MCP-counter is said to be conceptually closest to - ten populations from one experiment, at the cost of all spatial information— “Information of the cells’ spatial localization, which is available in IHC experiments, is lost, however, when using transcriptomic technologies. Histological confirmation of MCP-counter estimates may thus be necessary in cases where contamination of samples by surrounding tissues is unavoidable.”claim_relations[3]
sensewhat an MCP-counter score is - IN THE ABSTRACT: a robust quantification of the ABSOLUTE abundance of ten cell populations— “We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support…senses[0]
sensewhat an MCP-counter score is - IN THE RESULTS: a measure of the INTER-SAMPLE RELATIVE abundance of one population— “We designed a method, called MCP-counter, with the objective of measuring the inter-sample relative abundance of different cell populations in a microenvironment. To develop and validate our method, we designed a seven step strategy (Fig. 1).”senses[1]
sensewhat an MCP-counter score is - IN THE DISCUSSION: linearly correlated with abundance, in ARBITRARY UNITS that depend on the gene expression platform, comparable only between samples run on the same platform— “MCP-counter scores linearly correlate with the corresponding cell population abundances across samples, but they are expressed in arbitrary units. These arbitrary units are dependent on the gene expression platform used to produce the data and one can only compare samples produced with the same gene expression platforms.”senses[2]
sensewhat the score answers - MCP-COUNTER: is there more of this cell type in sample A than in sample Bsenses[3]
sensewhat the score answers - CIBERSORT: what share of the leukocytes in THIS sample are of this cell type. The paper's own simulation shows both answering correctly at once
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senses[4]
control742 tumour cell line samples from 21 non-hematopoietic cancer types included as negative controls, with no hematopoietic line included and the reason stated — held
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— rules out a marker that is merely abundant being taken for a marker that is specific - and the exclusion of hematopoietic lines removes a control that would have looked like the positive class
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controls[0]
controlthree simultaneous thresholds per candidate feature - a log2 expression difference, an AUC for identifying positive samples, and a signal-to-noise ratio — held
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— rules out a feature qualifying on one favourable statistic; all three must be met at once, which is what makes the marker set conservative rather than large
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controls[1]
controltwo independent validation series on different array platforms, 4804 samples, with the selection criteria re-applied to see whether the same markers come out — held
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— rules out the marker sets being an artefact of the discovery data or of one array platform - the overlap is tested for significance rather than eyeballed
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controls[2]
controlan in vitro RNA mixture experiment with the populations laid out in two transposed latin squares — held
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— rules out collinearity between the populations - without the latin square, a score could track its own population or any other that happened to co-vary with it
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controls[3]
controlimmunohistochemical digital quantification of three cell densities on tissue sections from 38 colorectal tumours, compared against the corresponding scores — held
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— rules out in vitro mixtures standing in for tissue - purified RNA mixed in a tube is not a tumour, and the ex vivo check is on real infiltrate
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controls[4]
controla simulation in which the within-leukocyte proportions are held constant and equal while the tumour fraction varies, run through both methods — held
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— rules out the two methods being compared on a case that cannot separate them - this one is built so that the correct answers differ, and both give theirs
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controls[5]
controlmarker sets from two previously published methods scored on the same three microenvironment series as this one — held
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— rules out the comparison being against the competitors' published numbers rather than against their markers on identical data
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controls[6]
controla limit of detection established per population against non-hematopoietic control samples — held
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— rules out a score being read as presence at any level - below the stated fraction the method cannot distinguish the population from a sample containing none
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controls[7]
controlthe same tumour analysis repeated on three transcriptomic platforms across more than 19,000 samples — partial
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— rules out a platform-specific artefact being reported as biology, within the constraint that the units themselves are not comparable across platforms
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controls[8]
controlthe discovery set spanning 344 different culture conditions, purification methods and cell treatments — held — rules out marker genes selected because of the conditions the reference cells were grown in rather than because of the cell type
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controls[9]
weightmethods that characterise proportions of immune populations within the leukocyte fraction while omitting the overall abundance of leukocytes in the sample — 0.95
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claim_relations[0]
weightCIBERSORT - not as wrong but as answering a different question; the paper's own simulation has it estimating stable within-leukocyte proportions correctly while total abundance changes — 0.95
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claim_relations[1]
weightits own headline - the abstract says absolute abundance and the discussion says arbitrary, platform-dependent units comparable only within a platform — 0.95
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claim_relations[2]
weightimmunohistochemistry, which MCP-counter is said to be conceptually closest to - ten populations from one experiment, at the cost of all spatial information — 0.9
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claim_relations[3]
differs in (stated by the tagger, no quotation)MCP-counter —distinct-from→ CIBERSORT — whether the comparison licensed is between cell types inside one sample or between samples for one cell typeconcept_edges[0]
relationMCP-counter —distinct-from→ CIBERSORT — one measures proportions within a sample, the other abundance of one population across samples, and the second pays for it in arbitrary units
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concept_edges[0]
weightMCP-counter —distinct-from→ CIBERSORT — 0.95
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concept_edges[0]
differs in (stated by the tagger, no quotation)MCP-counter —distinct-from→ flow cytometry — which axis of comparison the measurement is built forconcept_edges[1]
relationMCP-counter —distinct-from→ flow cytometry — flow-cytometry-inspired methods describe the relative proportions of populations within one sample; this one is designed for the other axis
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concept_edges[1]
weightMCP-counter —distinct-from→ flow cytometry — 0.95
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concept_edges[1]
outcomecontrol — held
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controls[0]
outcomecontrol — held
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controls[1]
outcomecontrol — held
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controls[2]
outcomecontrol — held
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controls[3]
outcomecontrol — held
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controls[4]
outcomecontrol — held
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controls[5]
outcomecontrol — held
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controls[6]
outcomecontrol — held
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controls[7]
outcomecontrol — partial
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controls[8]
outcome (stated by the tagger, no quotation)control — held
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controls[9]
limitationthe scores are in arbitrary units that depend on the gene expression platform, so only samples produced on the same platform can be compared
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limitations[0]
limitationtranscriptomic markers are not guaranteed to exist for a given population, because the definition requires expression in one and only one cell type
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limitations[1]
limitationpopulations without publicly available negative controls were dropped from the method regardless of their interest
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limitations[2]
limitationthe breadth of the control set discards markers that would work in a restricted setting - NCAM1 is lost because nervous malignant cells express it
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limitations[3]
limitationspatial localisation is lost relative to immunohistochemistry, and histological confirmation may be needed where contamination by surrounding tissue is unavoidable
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limitations[4]
limitationaccuracy depends on the quality of the expression summary, and the populations estimated are usually rare, so high sequencing depth may be required
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limitations[5]
limitationthe prognostic meta-analysis is univariate and unadjusted for stage or treatment
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limitations[6]
limitationsurprising tissue-level results are referred back to histopathological knowledge rather than defended - the authors say the method should be compared against it
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limitations[7]
limitationthe cytotoxic lymphocyte population's mRNA was not controlled in the mixture experiment, so its validation is a linear model on the other populations rather than a direct calibration
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limitations[8]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
finding
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content_tags.findings[5]
finding
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content_tags.findings[6]
finding
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content_tags.findings[7]
quantitythe Pearson correlations between scores and known RNA proportions in the in vitro mixtures
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content_tags.quantities[0]
quantitythe AUC floor for every signature across 4804 validation samples
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content_tags.quantities[1]
quantitythe upper bound on the limit of detection for every population assayed - from 1/950 to 1/50 of total RNA depending on which
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content_tags.quantities[2]
quantitytumour cell line samples included as negative controls, from 21 non-hematopoietic cancer types
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content_tags.quantities[3]
variablearbitrary units
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content_tags.variables[0]
variablelimit of detection
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content_tags.variables[1]
definitiontranscriptomic markers
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content_tags.definitions[0]
definitionarbitrary units
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content_tags.definitions[1]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptMCP-counter — not defined in this paper's own words; the method introduced here - a per-population score computed on each sample independently, designed for comparison across samples rather than between cell types
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concepts[0]
concepttranscriptomic markers — not defined in this paper's own words; gene features expressed in one and only one cell population with little within-population variation - a definition restrictive enough that the paper says such markers may not exist for a given population at all
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concepts[1]
conceptcell population — not defined in this paper's own words; the unit of the estimate; which populations are estimable was decided by which had purified positive samples and appropriate negative controls in public repositories
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concepts[2]
conceptnegative controls — not defined in this paper's own words; samples known not to contain the population - 742 tumour cell lines from 21 anatomical origins here, and the availability of these is what decides which populations can be measured
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concepts[3]
conceptarbitrary units — not defined in this paper's own words; the scale of the output - proportional to abundance, with no absolute meaning, and dependent on the gene expression platform, so cross-platform comparison is excluded
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concepts[4]
conceptCIBERSORT — not defined in this paper's own words; the deconvolution method that estimates within-leukocyte proportions; correct at its own question and, by construction, blind to changes in total leukocyte content
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concepts[5]
conceptflow cytometry — not defined in this paper's own words; the technique whose logic CIBERSORT-style methods inherit - proportions within one sample - and which MCP-counter is deliberately not modelled on
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concepts[6]
conceptlimit of detection — not defined in this paper's own words; the smallest RNA fraction at which a population is distinguishable from non-hematopoietic controls - below 2% for every population assayed, and from 1/950 to 1/50 depending on which
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concepts[7]
conceptimmunohistochemical — not defined in this paper's own words; the ex vivo cell-density measurement used as an independent check, and the technique MCP-counter's estimates are said to be conceptually closest to
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concepts[8]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
AUC
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[0]
Pearson
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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