Proprietary methods and reliable results
AltraBio’s offering encompasses 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
- 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
2016
Brinza, Lilia; Djebali, Sophia; Tomkowiak, Martine; Mafille, Julien; Loiseau, Céline; Jouve, Pierre-Emmanuel; de Bernard, Simon; Buffat, Laurent; Lina, Bruno; Ottmann, Michèle; Rosa-Calatrava, Manuel; Schicklin, Stéphane; Bonnefoy, Nathalie; Lauvau, Grégoire; Grau, Morgan; Wencker, Mélanie; Arpin, Christophe; Walzer, Thierry; Leverrier, Yann; Marvel, Jacqueline
Immune signatures of protective spleen memory CD8 T cells Journal Article
In: Sci Rep, vol. 6, pp. 37651, 2016, ISSN: 2045-2322.
@article{pmid27883012,
title = {Immune signatures of protective spleen memory CD8 T cells},
author = {Lilia Brinza and Sophia Djebali and Martine Tomkowiak and Julien Mafille and Céline Loiseau and Pierre-Emmanuel Jouve and Simon de Bernard and Laurent Buffat and Bruno Lina and Michèle Ottmann and Manuel Rosa-Calatrava and Stéphane Schicklin and Nathalie Bonnefoy and Grégoire Lauvau and Morgan Grau and Mélanie Wencker and Christophe Arpin and Thierry Walzer and Yann Leverrier and Jacqueline Marvel},
doi = {10.1038/srep37651},
issn = {2045-2322},
year = {2016},
date = {2016-11-01},
urldate = {2016-11-01},
journal = {Sci Rep},
volume = {6},
pages = {37651},
abstract = {Memory CD8 T lymphocyte populations are remarkably heterogeneous and differ in their ability to protect the host. In order to identify the whole range of qualities uniquely associated with protective memory cells we compared the gene expression signatures of two qualities of memory CD8 T cells sharing the same antigenic-specificity: protective (Influenza-induced, Flu-TM) and non-protective (peptide-induced, TIM) spleen memory CD8 T cells. Although Flu-TM and TIM express classical phenotypic memory markers and are polyfunctional, only Flu-TM protects against a lethal viral challenge. Protective memory CD8 T cells express a unique set of genes involved in migration and survival that correlate with their unique capacity to rapidly migrate within the infected lung parenchyma in response to influenza infection. We also enlighten a new set of poised genes expressed by protective cells that is strongly enriched in cytokines and chemokines such as Ccl1, Ccl9 and Gm-csf. CCL1 and GM-CSF genes are also poised in human memory CD8 T cells. These immune signatures are also induced by two other pathogens (vaccinia virus and Listeria monocytogenes). The immune signatures associated with immune protection were identified on circulating cells, i.e. those that are easily accessible for immuno-monitoring and could help predict vaccines efficacy.},
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pubstate = {published},
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}
Bachy, Emmanuel; Urb, Mirjam; Chandra, Shilpi; Robinot, Rémy; Bricard, Gabriel; de Bernard, Simon; Traverse-Glehen, Alexandra; Gazzo, Sophie; Blond, Olivier; Khurana, Archana; Baseggio, Lucile; Heavican, Tayla; Ffrench, Martine; Crispatzu, Giuliano; Mondière, Paul; Schrader, Alexandra; Taillardet, Morgan; Thaunat, Olivier; Martin, Nadine; Dalle, Stéphane; Garff-Tavernier, Magali Le; Salles, Gilles; Lachuer, Joel; Hermine, Olivier; Asnafi, Vahid; Roussel, Mikael; Lamy, Thierry; Herling, Marco; Iqbal, Javeed; Buffat, Laurent; Marche, Patrice N; Gaulard, Philippe; Kronenberg, Mitchell; Defrance, Thierry; Genestier, Laurent
CD1d-restricted peripheral T cell lymphoma in mice and humans Journal Article
In: J Exp Med, vol. 213, no. 5, pp. 841–857, 2016, ISSN: 1540-9538.
@article{pmid27069116,
title = {CD1d-restricted peripheral T cell lymphoma in mice and humans},
author = {Emmanuel Bachy and Mirjam Urb and Shilpi Chandra and Rémy Robinot and Gabriel Bricard and Simon de Bernard and Alexandra Traverse-Glehen and Sophie Gazzo and Olivier Blond and Archana Khurana and Lucile Baseggio and Tayla Heavican and Martine Ffrench and Giuliano Crispatzu and Paul Mondière and Alexandra Schrader and Morgan Taillardet and Olivier Thaunat and Nadine Martin and Stéphane Dalle and Magali Le Garff-Tavernier and Gilles Salles and Joel Lachuer and Olivier Hermine and Vahid Asnafi and Mikael Roussel and Thierry Lamy and Marco Herling and Javeed Iqbal and Laurent Buffat and Patrice N Marche and Philippe Gaulard and Mitchell Kronenberg and Thierry Defrance and Laurent Genestier},
doi = {10.1084/jem.20150794},
issn = {1540-9538},
year = {2016},
date = {2016-05-01},
urldate = {2016-05-01},
journal = {J Exp Med},
volume = {213},
number = {5},
pages = {841--857},
abstract = {Peripheral T cell lymphomas (PTCLs) are a heterogeneous entity of neoplasms with poor prognosis, lack of effective therapies, and a largely unknown pathophysiology. Identifying the mechanism of lymphomagenesis and cell-of-origin from which PTCLs arise is crucial for the development of efficient treatment strategies. In addition to the well-described thymic lymphomas, we found that p53-deficient mice also developed mature PTCLs that did not originate from conventional T cells but from CD1d-restricted NKT cells. PTCLs showed phenotypic features of activated NKT cells, such as PD-1 up-regulation and loss of NK1.1 expression. Injections of heat-killed Streptococcus pneumonia, known to express glycolipid antigens activating NKT cells, increased the incidence of these PTCLs, whereas Escherichia coli injection did not. Gene expression profile analyses indicated a significant down-regulation of genes in the TCR signaling pathway in PTCL, a common feature of chronically activated T cells. Targeting TCR signaling pathway in lymphoma cells, either with cyclosporine A or anti-CD1d blocking antibody, prolonged mice survival. Importantly, we identified human CD1d-restricted lymphoma cells within Vδ1 TCR-expressing PTCL. These results define a new subtype of PTCL and pave the way for the development of blocking anti-CD1d antibody for therapeutic purposes in humans.},
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pubstate = {published},
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}