AltraBio provides end-to-end cytometry data analysis services to accelerate biopharma and clinical research. From high-dimensional flow cytometry and mass cytometry (IMC) to spectral data, we deliver automated gating, robust quality control, cross-sample statistical modeling, and output-guided biomarker discovery.
Comprehensive Cytometry Solutions: From Raw Data to Biomarkers
Automated Gating & Workflow Standardization with CytAutomaton
Custom gating automaton developed in 1 to 4 weeks, running 24/7 with fast processing times of 5 to 10 minutes per file. Ideal for scaling clinical trial data analysis.
Eliminate manual, time-consuming gating tasks so your scientific teams can focus on data interpretation and biological strategy.
Multi-dimensional population discrimination beyond simple 2D biplots. Once validated, your gating automaton is frozen with a unique serial number to ensure 100% reproducible results across all study sites.
Biomarker Discovery & Clinical Population Profiling
Validated algorithms to identify key cell subsets for complex endpoints, including Measurable Residual Disease (MRD) in hematological malignancies, predictor signatures for anti-CTLA-4 immunotherapies, and autoimmune disease stratification.
Automated identification of nested cell subsets across varying granularity levels—from broad memory CD8+ T cells to highly specific effector subsets.
Advanced batch-effect reduction incorporating clinical outcomes (e.g., patient response) to guide cluster identification, eliminating artifacts and maximizing biological insight.
Unsupervised Data Exploration & Statistical Modeling
Dimensionality reduction (t-SNE, UMAP, PCA, SPADE, MDS) and automated clustering algorithms to uncover novel cell populations without prior assumptions.
Machine learning and statistical modeling to quantify changes in cell population abundance and marker expression across cohorts or treatment arms.
Trusted by Biopharma Leaders & Clinical Researchers
« Supervised approach is really a mature approach, I think there is an absolute need for having this solution… »
« The quality of the results we got from AB was quite remarkable in the good »
« AltraBio is viewed as automating subject matter experts, with the ability to do the same work. It allows us to free up subject matter experts at scale so a subject matter expert doesn’t have to spend as much time doing gating or reviewing gatings, and it all hinges on the quality that they provide. So for us, that’s the biggest selling point; the quality allow us to be able to say, OK this technology is almost as good as subject matter experts in this domain, and the pricing and the speed make it such that it becomes a feasible solution for us to say we can free up the scientists to do other things and this part can be handled by AltraBio. »
« Automated gating is there… In 2018, when automated gating was discussed at CYTO, some people stood up and said: “No! This is not going to work. Gating has been done by scientists & experts, and you can’t just put a computer to do their job.” We know now that it is not true because the Altrabio’s solutions are now doing it. It is pretty amazing. »
« This is accurate; we can use it at scale, so we don’t have to do the manual gating. »
« They do that extra bit of QC on their hand; they also check the transfers and put that extra effort in to make sure that what we do is accurate »
« The work we do with AltraBio is a partnership. I’ll make an example of the last analysis that we did; there were some timelines that needed to be met and they stepped in 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 felt really like a partnership and I think we’re heading in that direction… »
« They do cutting-edge work, we clearly like the innovation part »
« In clinical trials, we could get thousands of samples for different panels… The scale is clearly so big, not many companies can do this kind of work in production mode, on the labor scale »
« They fit clients’ need. »
« Top of the field »
« They are highly efficient and agile; you won’t interact with much people, so they are quick to respond and provide high-quality service »
« If there is some problem, or troubleshooting is necessary, or some change in the workflow, they are very flexible.»
« Exceptional »
« We really appreciate AltraBio because they provide one-stop full service, so we don’t need to worry about a lot of former problems, and also quality of results »
Ready to Scale Your Cytometry Data Pipelines?
Discover how our tailored cytometry analysis solutions can streamline your clinical and preclinical workflows.
Our Publications
2021
Barturen, Guillermo; Babaei, Sepideh; Català-Moll, Francesc; Martínez-Bueno, Manuel; Makowska, Zuzanna; Martorell-Marugán, Jordi; Carmona-Sáez, Pedro; Toro-Domínguez, Daniel; Carnero-Montoro, Elena; Teruel, María; Kerick, Martin; Acosta-Herrera, Marialbert; Lann, Lucas Le; Jamin, Christophe; Rodríguez-Ubreva, Javier; García-Gómez, Antonio; Kageyama, Jorge; Buttgereit, Anne; Hayat, Sikander; Mueller, Joerg; Lesche, Ralf; Hernandez-Fuentes, Maria; Juarez, Maria; Rowley, Tania; White, Ian; Marañón, Concepción; Anjos, Tania Gomes; Varela, Nieves; Aguilar-Quesada, Rocío; Garrancho, Francisco Javier; López-Berrio, Antonio; Maresca, Manuel Rodriguez; Navarro-Linares, Héctor; Almeida, Isabel; Azevedo, Nancy; Brandão, Mariana; Campar, Ana; Faria, Raquel; Farinha, Fátima; Marinho, António; Neves, Esmeralda; Tavares, Ana; Vasconcelos, Carlos; Trombetta, Elena; Montanelli, Gaia; Vigone, Barbara; Alvarez-Errico, Damiana; Li, Tianlu; Thiagaran, Divya; Alonso, Ricardo Blanco; Martínez, Alfonso Corrales; Genre, Fernanda; Mejías, Raquel López; Gonzalez-Gay, Miguel A; Remuzgo, Sara; Garcia, Begoña Ubilla; Cervera, Ricard; Espinosa, Gerard; Rodríguez-Pintó, Ignasi; Langhe, Ellen De; Cremer, Jonathan; Lories, Rik; Belz, Doreen; Hunzelmann, Nicolas; Baerlecken, Niklas; Kniesch, Katja; Witte, Torsten; Lehner, Michaela; Stummvoll, Georg; Zauner, Michael; Aguirre-Zamorano, Maria Angeles; Barbarroja, Nuria; Castro-Villegas, Maria Carmen; Collantes-Estevez, Eduardo; de Ramon, Enrique; Quintero, Isabel Díaz; Escudero-Contreras, Alejandro; Roldán, María Concepción Fernández; Gómez, Yolanda Jiménez; Moleón, Inmaculada Jiménez; Lopez-Pedrera, Rosario; Ortega-Castro, Rafaela; Ortego, Norberto; Raya, Enrique; Artusi, Carolina; Gerosa, Maria; Meroni, Pier Luigi; Schioppo, Tommaso; Groof, Aurélie De; Ducreux, Julie; Lauwerys, Bernard; Maudoux, Anne-Lise; Cornec, Divi; Devauchelle-Pensec, Valérie; Jousse-Joulin, Sandrine; Jouve, Pierre-Emmanuel; Rouvière, Bénédicte; Saraux, Alain; Simon, Quentin; Alvarez, Montserrat; Chizzolini, Carlo; Dufour, Aleksandra; Wynar, Donatienne; Balog, Attila; Bocskai, Márta; Deák, Magdolna; Dulic, Sonja; Kádár, Gabriella; Kovács, László; Cheng, Qingyu; Gerl, Velia; Hiepe, Falk; Khodadadi, Laleh; Thiel, Silvia; de Rinaldis, Emanuele; Rao, Sambasiva; Benschop, Robert J; Chamberlain, Chris; Dow, Ernst R; Ioannou, Yiannis; Laigle, Laurence; Marovac, Jacqueline; Wojcik, Jerome; Renaudineau, Yves; Borghi, Maria Orietta; Frostegård, Johan; Martín, Javier; Beretta, Lorenzo; Ballestar, Esteban; McDonald, Fiona; Pers, Jacques-Olivier; Alarcón-Riquelme, Marta E
Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases Journal Article
In: Arthritis Rheumatol, vol. 73, no. 6, pp. 1073–1085, 2021, ISSN: 2326-5205.
@article{pmid33497037,
title = {Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases},
author = {Guillermo Barturen and Sepideh Babaei and Francesc Català-Moll and Manuel Martínez-Bueno and Zuzanna Makowska and Jordi Martorell-Marugán and Pedro Carmona-Sáez and Daniel Toro-Domínguez and Elena Carnero-Montoro and María Teruel and Martin Kerick and Marialbert Acosta-Herrera and Lucas Le Lann and Christophe Jamin and Javier Rodríguez-Ubreva and Antonio García-Gómez and Jorge Kageyama and Anne Buttgereit and Sikander Hayat and Joerg Mueller and Ralf Lesche and Maria Hernandez-Fuentes and Maria Juarez and Tania Rowley and Ian White and Concepción Marañón and Tania Gomes Anjos and Nieves Varela and Rocío Aguilar-Quesada and Francisco Javier Garrancho and Antonio López-Berrio and Manuel Rodriguez Maresca and Héctor Navarro-Linares and Isabel Almeida and Nancy Azevedo and Mariana Brandão and Ana Campar and Raquel Faria and Fátima Farinha and António Marinho and Esmeralda Neves and Ana Tavares and Carlos Vasconcelos and Elena Trombetta and Gaia Montanelli and Barbara Vigone and Damiana Alvarez-Errico and Tianlu Li and Divya Thiagaran and Ricardo Blanco Alonso and Alfonso Corrales Martínez and Fernanda Genre and Raquel López Mejías and Miguel A Gonzalez-Gay and Sara Remuzgo and Begoña Ubilla Garcia and Ricard Cervera and Gerard Espinosa and Ignasi Rodríguez-Pintó and Ellen De Langhe and Jonathan Cremer and Rik Lories and Doreen Belz and Nicolas Hunzelmann and Niklas Baerlecken and Katja Kniesch and Torsten Witte and Michaela Lehner and Georg Stummvoll and Michael Zauner and Maria Angeles Aguirre-Zamorano and Nuria Barbarroja and Maria Carmen Castro-Villegas and Eduardo Collantes-Estevez and Enrique de Ramon and Isabel Díaz Quintero and Alejandro Escudero-Contreras and María Concepción Fernández Roldán and Yolanda Jiménez Gómez and Inmaculada Jiménez Moleón and Rosario Lopez-Pedrera and Rafaela Ortega-Castro and Norberto Ortego and Enrique Raya and Carolina Artusi and Maria Gerosa and Pier Luigi Meroni and Tommaso Schioppo and Aurélie De Groof and Julie Ducreux and Bernard Lauwerys and Anne-Lise Maudoux and Divi Cornec and Valérie Devauchelle-Pensec and Sandrine Jousse-Joulin and Pierre-Emmanuel Jouve and Bénédicte Rouvière and Alain Saraux and Quentin Simon and Montserrat Alvarez and Carlo Chizzolini and Aleksandra Dufour and Donatienne Wynar and Attila Balog and Márta Bocskai and Magdolna Deák and Sonja Dulic and Gabriella Kádár and László Kovács and Qingyu Cheng and Velia Gerl and Falk Hiepe and Laleh Khodadadi and Silvia Thiel and Emanuele de Rinaldis and Sambasiva Rao and Robert J Benschop and Chris Chamberlain and Ernst R Dow and Yiannis Ioannou and Laurence Laigle and Jacqueline Marovac and Jerome Wojcik and Yves Renaudineau and Maria Orietta Borghi and Johan Frostegård and Javier Martín and Lorenzo Beretta and Esteban Ballestar and Fiona McDonald and Jacques-Olivier Pers and Marta E Alarcón-Riquelme},
doi = {10.1002/art.41610},
issn = {2326-5205},
year = {2021},
date = {2021-06-01},
urldate = {2021-06-01},
journal = {Arthritis Rheumatol},
volume = {73},
number = {6},
pages = {1073--1085},
abstract = {OBJECTIVE: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis.
METHODS: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time.
RESULTS: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient.
CONCLUSION: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
METHODS: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time.
RESULTS: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient.
CONCLUSION: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.
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}
}
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}
}
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.