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Publications

ScaiVision core machine learning network papers:

Network Architecture:

Sensitive detection of rare disease-associated cell subsets via representation learning (Nature Communications)
Original manuscript establishing the framework which laid the groundwork for our biomarker discovery platform ScaiVision. Initially designed for CyTOF data, we have now extended this framework for the integrative analysis of a large variety of data modalities enabling true multimodal analyses crucial for precision medicine.
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Comparative immune profiling of acute respiratory distress syndrome patients with or without SARS-CoV-2 infection (Cell Reports Medicine)
In this study, exploiting ScaiVision’s unique sensitivity, we were able to identify an immune population strongly linked to the development of complications from COVID associated with a high mortality rate.
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Tracing Endometriosis: Coupling deeply phenotyped, single-cell based Endometrial Differences and AI for disease pathology and prediction
Scailyte’s single-cell atlas reveals key gene changes in endometriosis, offering new insights into pathophysiology, predictive models, and potential treatment targets.
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Integrating single-cell transcriptomics with Artificial Intelligence reveals pan-cancer biomarkers of brain metastasis
Scailyte's pan-cancer brain metastasis signature, derived via single-cell RNA sequencing and ScaiVision AI, enables early detection, prognosis, and targeted therapy.
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Single-Cell RNA Sequencing of PBMCs Identified Junction Plakoglobin (JUP) as Stratification Biomarker for Endometriosis
This study aimed to identify unique characteristics in the peripheral blood mononuclear cells (PBMCs) of endometriosis patients and develop a non-invasive early diagnostic tool. Using single-cell RNA sequencing (scRNA-seq), we constructed the first single-cell atlas of PBMCs from endometriosis patients based on 107,964 cells and 25,847 genes.
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Use Cases

Identification of ALP+/CD73+ defining markers for enhanced osteogenic potential in human adipose-derived mesenchymal stromal cells by mass cytometry.
Adipose-derived mesenchymal stem cells represent a relatively novel cell therapeutic modality for promoting bone healing, however they remain relatively uncharacterized on a subpopulation level. Using the forefather algorithm of ScaiVision, CellCNN, the authors were able to exploit CyTOF data to identify an ALP and CD73-double positive population as having a high bone differentiation potential. They could also show that these cells could not only be used to identify promising cell lines for use in bone regeneration, but their levels could also be used as a quality control during manufacturing.
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High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy
Although it has driven a paradigm shift in how cancer is treated, targeted immunotherapy is oftentimes plagued by low response rates, highlighting the urgent need for biomarkers predictive of response. Here, CellCNN was used to further characterize and confirm the identity of a specific subpopulation of monocytes as being strongly associated with response to anti-PD-1 immunotherapy in stage IV melanoma, paving the road for the selection of tailored treatments for patients.
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GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis (Nature Medicine)
Using the core machine learning algorithm underlying ScaiVision, our co-founder Prof. Claassen and coworkers were able to identify a novel T-cell population involved in multiple sclerosis expressing specific markers which allows them to attack the central nervous system. This discovery could help drive faster diagnosis and future targeted therapies.
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CellCnn

White papers

Scailyte’s ScaiVision performs best-in-class at sample class prediction
ScaiVision performs as the best-in-class algorithm at identifying molecular biomarkers, which accurately predict clinical status of the samples. Analysis with ScaiVision unlocks an unparalleled level of high-resolution and clinically relevant discoveries in single-cell datasets.
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True precision medicine through single-cell science
Scailyte’s artificial intelligence-powered platform, ScaiVision, identifies disease signatures that predict drug efficacy and describe drug mode of action to improve clinical outcomes for patients with complex diseases.
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AI-driven single-cell data analysis identifies a cell signature predictive of neurotoxicity and clinical response in CAR-T cell therapy of DLBCL
Delve deep into the transformative impact of ScaiVision, Scailyte's pioneering platform, on the realm of CAR-T cell therapy for Diffuse Large B-Cell Lymphoma (DLBCL).
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Posters

Mass cytometry and machine learning delineate tumor-defining cells in Sézary syndrome to allow discrimination from benign erythroderma (European Journal of Cancer)
Sezary syndrome is a cutaneous (skin) lymphoma which can be hard to distinguish from atopic dermatitis, a common skin condition. Our platform was able to identify rare tumor-related cells in blood from patients with early stages of Sezary syndrome enabling the rapid and sensitive diagnosis of this life-threatening condition.
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Single-cell analysis and deep learning reveal a novel diagnostic biomarker for endometriosis
With a time-to-diagnosis of close to 10 years, endometriosis is a common disease which can be severely debilitating. ScaiVision helped discover novel biomarkers which have the potential to facilitate this diagnosis using a simple blood draw.
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Targets & Cell Types in Immuno-Oncology Europe 2023

Presentation details

Title: Biomarker Discovery By Single-Cell Multi-Omics Analysis And AI

Diana Stoycheva , Principal Expert in Immunology & Single-cell Analysis at Scailyte 

Tuesday, 14th November 2023

NextGen Omics 2023

Presentation details

Title: Generating Clinically Relevant Insights Through Integrating Single-Cell Multi-Omics Data And Artificial Intelligence

Dennis Goehlsdorf, Machine Learning Engineer at Scailyte

Thursday, 9th November 2023

BioTech X 2023

Presentation details

Title: Enabling Precision Medicine by Combining Single-cell Multi-Omics Data Analysis and Artificial Intelligence

Diana Stoycheva , Principal Expert in Immunology & Single-cell Analysis at Scailyte 

Wednesday, 4th October 2023

LSX Congress USA 2023

Presentation details

Track: AI / Machine Learning / Advanced Software (Techbio), Oncology, Big Data / Blockchain / Predictive Analytics

Peter Nestorov, Founder & CEO, at Scailyte 

Wednesday, 13th September 2023

IO Summit 2023

Presentation details

Title: Enabling Precision Medicine by combining multi-omics analysis and artificial intelligence

Diana Stoycheva , Principal Expert in Immunology & Single-cell Analysis at Scailyte 

Tuesday, 21st June 2023

Future Labs Live 2023

Presentation details

Title: Integrating AI into the drug discovery workflow from biomarker treatment response to drug MOA identification

Cinzia Donato , R&D Manager Women’s Health at Scailyte 

Tuesday, 2nd June 2023

NextGen Omics Boston 2023

Presentation details

Title: Discovering next-generation biomarkers through integrating single-cell ‘omics and artificial intelligence.

Sarah Carl, Director of Data Science at Scailyte 

Tuesday, 30th March 2023

Bio-Europe Spring 2023

Presentation details

Title: Enabling precision medicine by combining multi-omics analysis and artificial intelligence

Corinne Solier, SVP Corporate & Technology Development at Scailyte 

Tuesday, 22th March 2023

Biomarkers UK 2023

Presentation details

Title: Discovering next-generation biomarkers through integrating single-cell ‘omics and artificial intelligence.

Sarah Carl, Director of Data Science at Scailyte 

Tuesday, 28th February 2023

BioTechX 2022

Discovering next-generation biomarkers through integration of single-cell biology and artificial intelligence
Single-cell technologies have enabled the generation of vast amounts of data from human tissues with unprecedented resolution. While numerous cell atlas efforts have made strides toward describing and cataloging cellular complexity, the full potential of single-cell data in biomarker discovery and clinical applications has yet to be unlocked, in part due to the challenges in analyzing such high-dimensional, complex data. Scailyte has developed a novel approach combining single-cell analysis, integration of clinical data, and a tailor-made supervised machine learning platform, which allows for targeted and sensitive biomarker discovery. Here we highlight several discovery projects across different disease areas, including oncology and cell therapy.
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Presentation details

Title: Discovering next-generation biomarkers through integration of single-cell biology and artificial intelligence.

Sarah Carl, Director of Data Science at Scailyte

Tuesday, 8th November 2022

Brochures

Generating clinically relevant insights from single-cell data
ScaiVision performs as the best-in-class algorithm at identifying molecular biomarkers, which accurately predict clinical status of the samples. Analysis with ScaiVision unlocks an unparalleled level of high-resolution and clinically relevant discoveries in single-cell datasets.
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ScaiVision AI platform: project workflow to facilitate drug development
ScaiVision unravels hidden secrets of complex single-cell multiomics data to extract composite biomarkers associated with different cell populations. Using a convolutional neural network and representation learning, ScaiVision automatically learns molecular patterns associated with relevant clinical outcomes. These signatures can then be applied to classify new samples and develop diagnostic assay prototypes.
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Single-cell analysis in CAR T cell therapy
Scailyte leverages the power of single-cell technologies and its proprietary data analysis platform ScaiVision. This union enables single-cell level precision in R&D and development of novel targeted assays in manufacturing of cell therapies. While cutting production and treatment costs of up to 50%, Scailyte’s solutions for patient stratification, optimised R&D and QA processes will make cell therapy accessible to more patients with maximum treatment efficacy.
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Webinars

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