A platform for serum antibody repertoire analysis for autoimmune disease diagnosis - systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects more than 200,000 people in the…

The National Institute of Allergy and Infectious Diseases awarded Abterra Biosciences, Inc. $306,555 on August 17, 2026, under the Allergy and Infectious Diseases Research program (CFDA 93.855) to develop a serum antibody repertoire analysis platform for diagnosing systemic lupus erythematosus.

Abterra will develop CDR3-SEQ, a novel technology platform that profiles over 1,000 antibody sequences from serum by analyzing the complementarity determining region 3 (CDRH3), the key domain for antigen recognition. The platform integrates innovations in sample preparation, mass spectrometry analysis, and computational tools to enable comprehensive, target-agnostic profiling of the serum antibody repertoire. As proof of concept, Abterra will build and evaluate three machine learning models to diagnose SLE from a single blood draw. The framework is designed to extend to other patient cohorts where serum antibody repertoire contributes to disease progression. Current SLE diagnosis takes an average of six years; this work targets elimination of prolonged diagnostic uncertainty through a single diagnostic test.

The recipient is Abterra Biosciences, Inc., operating as Digital Proteomics, located in San Diego, California. Performance runs through July 31, 2027. The award is assistance type Project Grant, a competitive mechanism under NIAID supporting biomedical research in infectious and allergic diseases at nonprofit and for-profit institutions.

A platform for serum antibody repertoire analysis for autoimmune disease diagnosis - systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects more than 200,000 people in the united states, but due to symptomatic differences between patients and similarity of symptoms with other diseases, IT takes on average 6 years for a patient to be diagnosed. Autoantibodies are known to play a role in SLE as well as other autoimmune diseases, sometimes emerging a decade before diagnosis. Over 100 self antigens have been associated with SLE and as part of diagnosis patient serum is screened for autoantibodies to a handful of these antigens. Despite their importance in diagnosis, and breadth of autoreactivity displayed by serum antibodies in SLE patients, there is no comprehensive method to profile the serum antibody repertoire in a target-agnostic way. In this proposal we will develop a method for profiling over 1,000 antibody sequences from serum. The complementarity determining region 3 (CDR3) of an antibody is the key domain for antigen recognition. The new technology, CDR3-SEQ, will focus on the CDRH3 region. To our knowledge this will be the most comprehensive analysis of the serum antibody repertoire ever attempted. Through a combination of innovations and optimizations around sample preparation, mass spectrometry analysis, and computational tools, we will develop a novel platform called CDR3-SEQ. In a proof-of-concept application of the value of CDR3-SEQ data, we will build and evaluate three machine learning models for diagnosing SLE from a single blood draw. The framework will be extendable to any patient cohort in which the serum antibody repertoire plays a role in disease progression. For SLE in particular, this proof-of-concept will be the first step towards a single diagnostic test that will eliminate years of uncertainty due to prolonged disease diagnosis with current protocols.