Des méthodes propriétaires et des résultats fiables
L’offre d’AltraBio couvre l’ensemble du flux d’analyse de données de cytométrie en flux, spectrale et de masse, allant du gating automatique à l’identification de biomarqueurs, en passant par le contrôle qualité des données et les analyses de différentiel d’abondance.
Vous souhaitez appliquer votre stratégie de gating sur un grand nombre de fichiers
- 1 à 4 semaines pour générer un automate de gating dédié
- traitement rapide (5-10 min/fichier), 24/7
- Utilisez votre expertise dans le développement de nouvelles stratégies et/ou dans l’interprétation biologique de vos résultats plutôt que dans l’étape de gating
- Nos automates réalisent leurs gating en considérant tous les marqueurs de l’étude. Cela permet d’avoir une meilleure vue sur les populations de cellules que celle qu’on peut avoir en n’utilisant que des biplots.
- Une fois ses performances validées, votre automate de gating sera figé avant d’être utilisé sur tous vos fichiers. Des mises à jour sont toujours possibles mais conduiront à un nouvel automate avec un nouveau numéro de série.
- Grâce à l’automatisation, l’utilisation de la cytométrie dans vos études cliniques, dans lesquelles vous envisagez un grand nombre de fichiers, ne sera plus un problème.
Vous voulez identifier des populations cellulaires marqueurs pour diagnostiquer une maladie ou prédire la réponse à un traitement
Nos solutions validées ont identifié les populations cellulaires pertinentes pour réaliser:
- l’évaluation de la maladie résiduelle mesurable (MRD) dans différents cancers du sang.
- la prediction des patients répondeurs au médicament anti-cancer anti-CTL4 .
- le diagnostic d’une maladie autoimmune.
Notre méthode est capable d’identifier des sous-ensembles de cellules discriminantes à différentes granularités le long d’un axe de différenciation cellulaire, ce qui donne des sous-ensembles de cellules imbriquées : par exemple dans la population de cellules T, les sous-ensembles pertinents peuvent aller du sous-ensemble plus large de cellules CD8 mémoire au plus spécifique. sous-ensemble intégré de la mémoire effectrice CD8.
- Notre approche est moins sensible aux effets de lot.
- Notre méthode peut utiliser des informations supplémentaires telles que les résultats des patients (par exemple, le statut des patients) pour guider intelligemment l’identification des clusters afin d’augmenter encore la pertinence des populations identifiées et d’éviter les faux artefacts.
Vous voulez explorer vos données de cytométrie
- Gating par réduction de dimension : Analyse en composante principale, Minimum Spanning Tree layouts (e.g. SPADE), Multi Dimensional Scaling, t-stochastic neighbor embeddings (e.g. ViSNE), UMAP, etc.
- Clustering: approches basées sur la topologie/le graphe (e.g. SamSPECTRAL), approches basées sur la densité (e.g. Flock), approches basées sur le modèle (e.g. immunoClust, FLAME, FlowClust, flowMerge), approches hybrides (e.g. FlowSOM, Phenograph, FlowPeaks, FlowMeans, etc.), approches d’ensemble, etc.
- Modélisation statistique
- Modèles linéaires généralisés, modèles mixtes (etc) pour (1) l’analyse différentielle d’abondance de populations cellulaires ou (2) l’analyse différentielle de marqueurs d’expression stratifiés par populations cellulaires.
- Machine learning
- Apprentissage supervisé (e.g., Forêt aléatoire, Boosting, SVM, (sparse) PLS), identification de correlation , etc.
- Algorithmes dédiés à des tâches spécifiques
- CITRUS, RchyOptimyx, etc.
Témoignages
« Ils sont très efficaces et agiles, vous n’interagirez pas avec beaucoup de monde donc ils réagissent rapidement et fournissent un service de haute qualité »
“Ils font ce petit contrôle qualité supplémentaire sur leur main, ils vérifient également les transferts, ils mettent cet effort supplémentaire pour s’assurer que ce que nous faisons est exact”
« Le travail que nous faisons jusqu’ici avec AltraBio, c’est du partenariat. … il y avait des échéanciers à respecter et ils sont intervenus et ont dit OK, nous le ferons dans quelques jours, pas dans une semaine, pas dans un mois… quand vous avez cette relation, quand vous comprenez le valeur et vous comprenez les délais du client, cela ressemblait vraiment à un partenariat”
Nos publications en cytometrie
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 Article de journal
Dans: Nat Commun, vol. 12, no. 1, p. 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}
}
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 Article de journal
Dans: Ann Rheum Dis, vol. 80, no. 1, p. 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}
}
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 Article de journal
Dans: Ann Rheum Dis, vol. 79, no. 9, p. 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}
}
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.