Need Expert Biological Data Analysis?
AltraBio specializes in biological data analysis, helping you unlock insights from cytometry, omics, and medical data using AI and advanced statistics.
We serve as a research and development partner for leading companies and university hospitals across various sectors, including pharmaceuticals, medical devices, diagnostics, and dermo-cosmetics.
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Our Biological Data Analysis Expertise
Why Choose AltraBio for Biological Data Analysis?
« Even in the age of generative AI, Altrabio’s two decades of expertise in maths, stats, biology, and medical science remain invaluable. They don’t just talk, they do. No flashy marketing, no inflated costs, just solid, thoughtful work from study design to actionable insights. A trusted partner, for twenty years, in a world full of noise. Highly recommend working with them to make real sense of your complex biomedical and omics data. »
« 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 »
« 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.»
« 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… »
Discover how our tailored solutions in biological data analysis can accelerate your R&D projects.
Latest Publications
2026
Systems vaccinology analysis of saRNA immunization identifies an acute innate immune signature correlated with adaptive immunity Journal Article
In: Molecular Therapy Advances, vol. 34, no. 1, 2026, ISSN: 3117-387X.
@article{Elliott2026,
title = {Systems vaccinology analysis of saRNA immunization identifies an acute innate immune signature correlated with adaptive immunity},
author = {Tamara Elliott and Ziyin Wang and Olivia Bonduelle and Abbey Evans and Suzanne Day and Leon R. McFarlane and Simon de Bernard and Karine Alves and Julien Nourikyan and Michele Wokam and Katrina Pollock and Hannah M. Cheeseman and Behazine Combadiere and Robin J. Shattock and John S. Tregoning},
doi = {10.1016/j.omta.2026.201706},
issn = {3117-387X},
year = {2026},
date = {2026-03-12},
urldate = {2026-03-12},
journal = {Molecular Therapy Advances},
volume = {34},
number = {1},
publisher = {Elsevier BV},
abstract = {Self-amplifying ribonucleic acid (saRNA) vaccines are a next-generation RNA vaccine platform with great potential. Systems vaccinology provides a potent tool to interrogate vaccine-induced responses in volunteers and to dissect the mechanisms by which vaccines elicit a protective immune response or cause reactogenicity. In the current study, we performed transcriptomic analysis on blood samples collected from volunteers vaccinated as part of a phase I study of an saRNA vaccine expressing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike antigen. We observed significant gene over-expression following both the prime and boost vaccinations. Over-expressed genes were predominantly associated with type I interferon signaling pathways and innate immune cell recruitment. This transcriptomic signature was reflected by an increase in cytokines in the plasma at the same time points and a significant increase in monocytes in the blood, both of which correlated with the antibody response to the vaccine. When individuals were segregated by the degree of reactogenicity, we also detected differences in gene expression related to immune responses. Overall, results show that saRNA induces a potent, acute inflammatory response with similarities to other RNA vaccines, and it will be important to further dissect the role of the over-expressed genes in immunogenicity and reactogenicity.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Prognostic biomarkers for idiopathic pulmonary fibrosis: findings from ISABELA clinical trials Journal Article
In: ERJ Open Res, vol. 12, no. 1, pp. 00893–2025, 2026, ISSN: 2312-0541.
@article{Randall2025,
title = {Prognostic biomarkers for idiopathic pulmonary fibrosis: findings from ISABELA clinical trials},
author = {Matthew J. Randall and Claus A. Andersen and Kevin K. Brown and Simon de Bernard and Paul Ford and Naftali Kaminski and Michael Kreuter and Sharlene Lim and Toby M. Maher and Niyati Prasad and Antje Prasse and Philippe Pujuguet and Vincenzo Teneggi and Bernt van den Blink and Louise V. Wain and Timothy R. Watkins and Wim Wuyts and Yasmina Bauer},
doi = {10.1183/23120541.00893-2025},
issn = {2312-0541},
year = {2026},
date = {2026-01-00},
urldate = {2026-01-00},
journal = {ERJ Open Res},
volume = {12},
number = {1},
pages = {00893--2025},
publisher = {European Respiratory Society (ERS)},
abstract = {Background
Idiopathic pulmonary fibrosis (IPF) is characterised by progressive loss of pulmonary function and poor survival. Although biomarkers for disease progression and mortality exist, their reliability in large studies remains unproven. This study investigates prognostic biomarkers from the ISABELA trials, the largest IPF cohort to date, to identify those predicting worse clinical outcomes.
Methods
Plasma from 1280 IPF patients in ISABELA 1 and 2 (NCT03711162, NCT03733444) was analysed for 17 circulating soluble disease-related biomarkers at multiple time-points and for the MUC5B (rs35705950_T) genotype. Statistical learning algorithms investigated biomarker levels/status with disease progression (≥10% decline in forced vital capacity (FVC) or mortality within 1 year) and pharmacotherapy.
Results
Patients with ≥10% annual decline in FVC had higher median baseline of matrix metalloproteinase-7 (MMP-7) versus those with <10% decline (5.5 versus 4.2 µg·L−1; p<0.005). Patients with baseline MMP-7 ≥5.2 μg·L−1 and/or C-C motif chemokine ligand 18 (CCL18) ≥75.2 μg·L−1 had increased risk of mortality (p<0.0001); with patients having both elevated biomarkers at an even greater risk. Machine learning identified CCL18 changes by week 26 as a predictor of disease progression. The rs35705950_T genotype predicted neither mortality nor disease progression.
Conclusions
We provide new insights into the prognostic value of MMP-7 and CCL18 in identifying high-risk IPF patients in the largest cohort to date. The combination of high baseline MMP-7 and CCL18 levels, along with longitudinal changes in CCL18, has the potential to enhance risk stratification and support efficacy assessment and monitoring in clinical trials.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Idiopathic pulmonary fibrosis (IPF) is characterised by progressive loss of pulmonary function and poor survival. Although biomarkers for disease progression and mortality exist, their reliability in large studies remains unproven. This study investigates prognostic biomarkers from the ISABELA trials, the largest IPF cohort to date, to identify those predicting worse clinical outcomes.
Methods
Plasma from 1280 IPF patients in ISABELA 1 and 2 (NCT03711162, NCT03733444) was analysed for 17 circulating soluble disease-related biomarkers at multiple time-points and for the MUC5B (rs35705950_T) genotype. Statistical learning algorithms investigated biomarker levels/status with disease progression (≥10% decline in forced vital capacity (FVC) or mortality within 1 year) and pharmacotherapy.
Results
Patients with ≥10% annual decline in FVC had higher median baseline of matrix metalloproteinase-7 (MMP-7) versus those with <10% decline (5.5 versus 4.2 µg·L−1; p<0.005). Patients with baseline MMP-7 ≥5.2 μg·L−1 and/or C-C motif chemokine ligand 18 (CCL18) ≥75.2 μg·L−1 had increased risk of mortality (p<0.0001); with patients having both elevated biomarkers at an even greater risk. Machine learning identified CCL18 changes by week 26 as a predictor of disease progression. The rs35705950_T genotype predicted neither mortality nor disease progression.
Conclusions
We provide new insights into the prognostic value of MMP-7 and CCL18 in identifying high-risk IPF patients in the largest cohort to date. The combination of high baseline MMP-7 and CCL18 levels, along with longitudinal changes in CCL18, has the potential to enhance risk stratification and support efficacy assessment and monitoring in clinical trials.
2025
Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists Journal Article
In: Platelets, vol. 36, no. 1, 2025, ISSN: 1369-1635.
@article{Cognasse2025,
title = {Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists},
author = {Fabrice Cognasse and Kim Anh Nguyen and Marco Heestermans and Charles-Antoine Arthaud and Marie-Ange Eyraud and Amelie Prier and Simon de Bernard and Julien Nourikyan and Anne-Claire Duchez and Stephane Avril and Olivier Garraud and Hind Hamzeh-Cognasse},
doi = {10.1080/09537104.2025.2572982},
issn = {1369-1635},
year = {2025},
date = {2025-10-27},
urldate = {2025-10-27},
journal = {Platelets},
volume = {36},
number = {1},
publisher = {Informa UK Limited},
abstract = {Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how platelets respond to different stimuli and to identify specific molecular signatures using computational approaches. Platelets from 10 healthy donors were stimulated under seven conditions, including TRAP (PAR-1), AYPGKF (PAR-4), ADP, collagen, sCD40L, fibrinogen, and a control. A total of 47 markers—encompassing membrane proteins, soluble mediators, and intracellular signals—were analyzed. Statistical and machine learning methods, including hierarchical clustering and random forest algorithms, were used to classify and interpret the data. Distinct activation profiles emerged for each agonist. A reduced panel of six markers (AKT, CD40L, CD62P, PKC, RANTES, and TSLP) enabled identification of the stimulus with 86.8% accuracy. Machine learning further improved classification (87.9% multiclass accuracy). Differences were also observed across donors, highlighting inter-individual variability. This work supports a new paradigm in which platelets act as “biological sensors,” fine-tuning their responses to environmental cues. The identified biomarker panel provides a basis for further investigation into the characterization of platelet activation profiles, with potential relevance for future diagnostic and therapeutic applications in thromboinflammatory and immune-mediated conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Funding
