Proprietary methods and reliable results
AltraBio’s offer covers the entire data analysis workflow for flow, spectral and mass cytometry data analysis from automated gating to cross-sample analysis, quality controls and biomarker identification.
You need to apply your gating strategy to a large number of files
- 1 to 4 weeks to generate a dedicated gating automaton
- Fast processing (5-10 min/file), 24/7
- Apply your expertise to the development of new strategies and/or the biological interpretation of your results instead of spending your time gating
- Our automata consider all the markers used in your study. This allows them to better discriminate cell populations than with biplots.
- Once its performance validated, your gating automaton will be frozen and then used on all the files of your study. Updates are still possible but will lead to a new automaton with a new serial number.
- Thanks to automation, the use of cytometry for large clinical studies is no longer a problem.
You need to identify marker cell populations for diagnosis or for predicting the response to a treatment
Our validated solutions identified the relevant cell populations to:
- evaluate the measurable residual disease (MRD) in different blood cancers.
- predict responder patients for the anti-CTL4 anti-cancer drug.
- diagnose an autoimmune disease.
Our methods are able to identify discriminative cell subsets at different granularities along an axis of cell differentiation resulting in nested cell subsets: for instance in the T cell population, the relevant subsets can range from the broader subset of memory CD8 cells to the more specific embedded subset of effector memory CD8.
- Our approach is less sensitive to batch effects.
- Our methods can incorporate additional information such as patient outcome to guide cluster identification to further increase the relevance of identified populations and avoid spurious artefacts.
You need to explore your cytometry data
- Gating by dimension reduction: Principal Component Analysis, Minimum Spanning Tree layouts (e.g. SPADE), Multi Dimensional Scaling, t-stochastic neighbor embeddings (e.g. ViSNE), UMAP, etc.
- Clustering: topological/graph-based approaches (e.g. SamSPECTRAL), density-based approaches (e.g. Flock), model-based approaches (e.g. immunoClust, FLAME, FlowClust, flowMerge), hybrid approaches (e.g. FlowSOM, Phenograph, FlowPeaks, FlowMeans, etc.), ensemble approaches, etc.
- Statistical modeling
- Generalized linear models, mixed models (etc) for (1) differential analysis of abundance of cell populations or (2) differential analysis of marker expression stratified by cell population.
- Machine learning
- Supervised learning (e.g., Random Forest, Boosting, SVM, (sparse) PLS), correlation identification, etc.
- Specific task-dedicated algorithms
- CITRUS, RchyOptimyx, etc.
Testimonials
« They are highly efficient and agile, you will interact with only a few people so they are quick to respond and provide high quality services »
« They do that extra bit of quality control, they also check the transfers, they put that extra effort to make sure that what we do is accurate »
« There were some timelines that needed to be met and they stepped up and said OK we‘ll get this done in a few days, not in a week, not in a month … when you have that relationship, when you understand the value and you understand the timelines of the customer, that really feels like a partnership”
Our Publications In Cytometry Data Analysis
2021
Soret, Perrine; Dantec, Christelle Le; Desvaux, Emiko; Foulquier, Nathan; Chassagnol, Bastien; Hubert, Sandra; Jamin, Christophe; Barturen, Guillermo; Desachy, Guillaume; Devauchelle-Pensec, Valérie; Boudjeniba, Cheïma; Cornec, Divi; Saraux, Alain; Jousse-Joulin, Sandrine; Barbarroja, Nuria; Rodríguez-Pintó, Ignasi; Langhe, Ellen De; Beretta, Lorenzo; Chizzolini, Carlo; Kovács, László; Witte, Torsten; Bettacchioli, Eléonore; Buttgereit, Anne; Makowska, Zuzanna; Lesche, Ralf; Borghi, Maria Orietta; Martin, Javier; Courtade-Gaiani, Sophie; Xuereb, Laura; Guedj, Mickaël; Moingeon, Philippe; Alarcón-Riquelme, Marta E; Laigle, Laurence; Pers, Jacques-Olivier
A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome Journal Article
In: Nat Commun, vol. 12, no. 1, pp. 3523, 2021, ISSN: 2041-1723.
@article{pmid34112769,
title = {A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome},
author = {Perrine Soret and Christelle Le Dantec and Emiko Desvaux and Nathan Foulquier and Bastien Chassagnol and Sandra Hubert and Christophe Jamin and Guillermo Barturen and Guillaume Desachy and Valérie Devauchelle-Pensec and Cheïma Boudjeniba and Divi Cornec and Alain Saraux and Sandrine Jousse-Joulin and Nuria Barbarroja and Ignasi Rodríguez-Pintó and Ellen De Langhe and Lorenzo Beretta and Carlo Chizzolini and László Kovács and Torsten Witte and Eléonore Bettacchioli and Anne Buttgereit and Zuzanna Makowska and Ralf Lesche and Maria Orietta Borghi and Javier Martin and Sophie Courtade-Gaiani and Laura Xuereb and Mickaël Guedj and Philippe Moingeon and Marta E Alarcón-Riquelme and Laurence Laigle and Jacques-Olivier Pers},
doi = {10.1038/s41467-021-23472-7},
issn = {2041-1723},
year = {2021},
date = {2021-06-01},
urldate = {2021-06-01},
journal = {Nat Commun},
volume = {12},
number = {1},
pages = {3523},
abstract = {There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.
Bossini-Castillo, Lara; Villanueva-Martin, Gonzalo; Kerick, Martin; Acosta-Herrera, Marialbert; López-Isac, Elena; Simeón, Carmen P; Ortego-Centeno, Norberto; Assassi, Shervin; Hunzelmann, Nicolas; Gabrielli, Armando; de Vries-Bouwstra, J K; Allanore, Yannick; Fonseca, Carmen; Denton, Christopher P; Radstake, Timothy Rdj; Alarcón-Riquelme, Marta Eugenia; Beretta, Lorenzo; Mayes, Maureen D; Martin, Javier
Genomic Risk Score impact on susceptibility to systemic sclerosis Journal Article
In: Ann Rheum Dis, vol. 80, no. 1, pp. 118–127, 2021, ISSN: 1468-2060.
@article{pmid33004331,
title = {Genomic Risk Score impact on susceptibility to systemic sclerosis},
author = {Lara Bossini-Castillo and Gonzalo Villanueva-Martin and Martin Kerick and Marialbert Acosta-Herrera and Elena López-Isac and Carmen P Simeón and Norberto Ortego-Centeno and Shervin Assassi and Nicolas Hunzelmann and Armando Gabrielli and J K de Vries-Bouwstra and Yannick Allanore and Carmen Fonseca and Christopher P Denton and Timothy Rdj Radstake and Marta Eugenia Alarcón-Riquelme and Lorenzo Beretta and Maureen D Mayes and Javier Martin},
doi = {10.1136/annrheumdis-2020-218558},
issn = {1468-2060},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Ann Rheum Dis},
volume = {80},
number = {1},
pages = {118--127},
abstract = {OBJECTIVES: Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.
METHODS: Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.
RESULTS: The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren's syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.
CONCLUSIONS: GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVES: Genomic Risk Scores (GRS) successfully demonstrated the ability of genetics to identify those individuals at high risk for complex traits including immune-mediated inflammatory diseases (IMIDs). We aimed to test the performance of GRS in the prediction of risk for systemic sclerosis (SSc) for the first time.
METHODS: Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.
RESULTS: The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren's syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.
CONCLUSIONS: GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.
METHODS: Allelic effects were obtained from the largest SSc Genome-Wide Association Study (GWAS) to date (9 095 SSc and 17 584 healthy controls with European ancestry). The best-fitting GRS was identified under the additive model in an independent cohort that comprised 400 patients with SSc and 571 controls. Additionally, GRS for clinical subtypes (limited cutaneous SSc and diffuse cutaneous SSc) and serological subtypes (anti-topoisomerase positive (ATA+) and anti-centromere positive (ACA+)) were generated. We combined the estimated GRS with demographic and immunological parameters in a multivariate generalised linear model.
RESULTS: The best-fitting SSc GRS included 33 single nucleotide polymorphisms (SNPs) and discriminated between patients with SSc and controls (area under the receiver operating characteristic (ROC) curve (AUC)=0.673). Moreover, the GRS differentiated between SSc and other IMIDs, such as rheumatoid arthritis and Sjögren's syndrome. Finally, the combination of GRS with age and immune cell counts significantly increased the performance of the model (AUC=0.787). While the SSc GRS was not able to discriminate between ATA+ and ACA+ patients (AUC<0.5), the serological subtype GRS, which was based on the allelic effects observed for the comparison between ACA+ and ATA+ patients, reached an AUC=0.693.
CONCLUSIONS: GRS was successfully implemented in SSc. The model discriminated between patients with SSc and controls or other IMIDs, confirming the potential of GRS to support early and differential diagnosis for SSc.
2020
Beretta, Lorenzo; Barturen, Guillermo; Vigone, Barbara; Bellocchi, Chiara; Hunzelmann, Nicolas; Langhe, Ellen De; Cervera, Ricard; Gerosa, Maria; Kovács, László; Castro, Rafaela Ortega; Almeida, Isabel; Cornec, Divi; Chizzolini, Carlo; Pers, Jacques-Olivier; Makowska, Zuzanna; Lesche, Ralf; Kerick, Martin; Alarcón-Riquelme, Marta Eugenia; Martin, Javier
Genome-wide whole blood transcriptome profiling in a large European cohort of systemic sclerosis patients Journal Article
In: Ann Rheum Dis, vol. 79, no. 9, pp. 1218–1226, 2020, ISSN: 1468-2060.
@article{pmid32561607,
title = {Genome-wide whole blood transcriptome profiling in a large European cohort of systemic sclerosis patients},
author = {Lorenzo Beretta and Guillermo Barturen and Barbara Vigone and Chiara Bellocchi and Nicolas Hunzelmann and Ellen De Langhe and Ricard Cervera and Maria Gerosa and László Kovács and Rafaela Ortega Castro and Isabel Almeida and Divi Cornec and Carlo Chizzolini and Jacques-Olivier Pers and Zuzanna Makowska and Ralf Lesche and Martin Kerick and Marta Eugenia Alarcón-Riquelme and Javier Martin },
doi = {10.1136/annrheumdis-2020-217116},
issn = {1468-2060},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
journal = {Ann Rheum Dis},
volume = {79},
number = {9},
pages = {1218--1226},
abstract = {OBJECTIVES: The analysis of annotated transcripts from genome-wide expression studies may help to understand the pathogenesis of complex diseases, such as systemic sclerosis (SSc). We performed a whole blood (WB) transcriptome analysis on RNA collected in the context of the European PRECISESADS project, aiming at characterising the pathways that differentiate SSc from controls and that are reproducible in geographically diverse populations.
METHODS: Samples from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were divided into a discovery (n=79+163; Southern Europe) and validation cohort (n=83+89; Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of Individual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes/pathways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated.
RESULTS: Overall, 15 224 genes and 1277 functional pathways were available; of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences; neutrophils were the major determinant of gene expression in SSc-WB samples.
CONCLUSIONS: We discovered a set of differentially expressed genes/pathways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
OBJECTIVES: The analysis of annotated transcripts from genome-wide expression studies may help to understand the pathogenesis of complex diseases, such as systemic sclerosis (SSc). We performed a whole blood (WB) transcriptome analysis on RNA collected in the context of the European PRECISESADS project, aiming at characterising the pathways that differentiate SSc from controls and that are reproducible in geographically diverse populations.
METHODS: Samples from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were divided into a discovery (n=79+163; Southern Europe) and validation cohort (n=83+89; Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of Individual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes/pathways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated.
RESULTS: Overall, 15 224 genes and 1277 functional pathways were available; of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences; neutrophils were the major determinant of gene expression in SSc-WB samples.
CONCLUSIONS: We discovered a set of differentially expressed genes/pathways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.
METHODS: Samples from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were divided into a discovery (n=79+163; Southern Europe) and validation cohort (n=83+89; Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of Individual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes/pathways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated.
RESULTS: Overall, 15 224 genes and 1277 functional pathways were available; of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences; neutrophils were the major determinant of gene expression in SSc-WB samples.
CONCLUSIONS: We discovered a set of differentially expressed genes/pathways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.