The indicator cell assay platform (iCAP) is a novel next-generation approach for blood-based diagnostics that uses standardized cells as biosensors to amplify weak disease signals in blood. We developed an Alzheimer's disease iCAP (AD-iCAP) for early detection at the mild cognitive impairment/mild dementia stages. To develop the assay, patient plasma is incubated with standardized neurons, which transduce complex circulating signals into gene-expression readouts used to train multivariate disease classifiers via machine learning. We applied systems biology analyses (e.g., GSEA, PCA, correlation/network analyses) to optimize analytical and computational parameters, and then evaluated a locked model in a study with retrospectively collected samples. Performance was AUC 0.64 (95% CI 0.51-0.78, n=82) on an independent external-validation set and AUC 0.77 (95% CI 0.57-0.96, n=23) on a blind set, supporting prospective confirmation in a larger cohort. To overcome pre-analytical noise and reduce bias in feature selection, modeling was done using a fixed panel of 84 candidate genes chosen a priori from an external AD-iCAP dataset generated with 5XFAD mouse plasma. Despite using no AD-specific prior knowledge in this approach, the assay readout was enriched for Alzheimer's-relevant pathways, including cholesterol biosynthesis, synaptic structure/neurotransmission and PIK3/AKT activation. Because the assay senses a multivalent cellular response, which is orthogonal to circulating amyloid or tau measurements, AD-iCAP may complement existing blood tests, and its multivariate readout offers a path to precision-medicine applications such as patient stratification for treatment response.

The funders provided support in the form of research materials and salaries but had no role in the study's design, data collection, analysis, decision to publish, or manuscript preparation. JJS, RJL, JDB, MDD, YQ, and GAW are current or past employees of PreCyte Inc. Additionally, JJS, JDA, JDB, MDD, YQ, RJL, SAD, GAW, and LRM have equity interests in PreCyte Inc., which is developing products related to the research described in this publication. The commercial affiliation with PreCyte Inc. does not alter our adherence to journal policies on data and material sharing. All other authors declare no competing interests. EP, GL, CLM, and AF report no conflicts of interest.

Research reported in this publication was supported by the National Institute of Aging (NIA) and National Cancer Institute (NCI) of the National Institutes of Health (NIH) under Award Numbers R44AG051282, R43CA203455, R44CA203455 and R43AG056215. We are grateful to three studies for providing patient plasma samples and clinical data for this research: 1) The AIBL study (www.AIBL.csiro.au) is a collaboration between CSlRO, Edith Cowan University (ECU), The Florey Institute of Neuroscience and Mental Health (FINMH), National Ageing Research Institute (NARI) and Austin Health. It also involves support from CogState Ltd., Hollywood Private Hospital, and Sir Charles Gairdner Hospital. The study received funding support from CSIRO, the Science and Industry Endowment Fund (www.SlEF.org.au), NHMRC and Dementia Collaborative Research Centres (DCRC), Alzheimer's Australia (AA), Alzheimer's Association and the McCusker Alzheimer's Research Foundation. 2) Knight Alzheimer's Disease Research Center (ADRC) at Washington University in Saint Louis, supported by the NIA under Award Numbers P50AG005681 and P01AG03991, and P01AG026276. 3) University of Washington ADRC, supported by the NIA under award Number P50AG05136, Pacific Northwest Udall Center (P50NS062684) and Department of Veterans Affairs. We thank all the participants who took part in this study and the clinicians who referred participants. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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WCG IRB waived ethical approval for this work

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All data produced in the present study are present in the paper and/or the Supplementary Materials, except RNA-seq data related to this paper will be available from GEO under the accession numbers after peer reviewed publication. R code for disease classification model M1 will be available at after peer reviewed publication.