Jordan, Clarissa E.

Abstract Background: Flow cytometry (FC) is widely utilized for the identification of measurable residual disease (MRD) in multiple myeloma (MM), providing an essential laboratory indicator for prognostic assessment and therapeutic management. Unfortunately, the high level of expertise required, long manual analysis time, and complexity of FC data files currently limit the offering of this testing to selected reference laboratories. Methods: We developed an artificial intelligence (AI) pipeline to automatically enhance unaltered FC files, minimizing the expertise and manual analysis time needed to detect MM MRD using the 2-tube Euroflow Consortium Panel (Cytognos). Raw FC files corresponding to 150 MRD-positive bone marrow samples (MRD < 5%, median = 0.0022%), and 39 MRD-negative samples were processed through our CCADDAS (Clustering and Classification of All events, Dimensionality reduction, Downsampling and Aberrancy Scaling) pipeline, deployed on a custom clinical-grade cloud environment (Biolegend). Automated processing steps included elimination of acquisition errors (FlowCut), state-of-the-art clustering (PARC), cluster-informed and density-based downsampling with preservation of low-event subsets, dimensionality reduction (UMAP), and cluster-based anomaly detection compared to negative controls (29 bone marrow aspirates). In addition, a deep neural network (DNN) trained on expert-defined subpopulations from negative controls was included for automatic gating of normal subsets (Tensorflow v2). AI-enhanced data files were analyzed using a general-purpose flow cytometry analysis software (Kaluza v 2.3, Beckman Coulter), and %MRD estimates were compared to reported results based on traditional expert analysis of original FC files. Results: Cluster-informed downsampling reduced the number of cells to be manually analyzed from 4.1 million to 196,761 cells per tube, on average (95% cellularity reduction); resulting in a smaller FC data file (from 203.2 MB to 20.5 MB; 90% data reduction). Importantly, low-level MRD events were adequately preserved after downsampling (median 88% and 77% retention, for 0.01-0.001% MRD and < 0.001% MRD, respectively). True number of events were accurately estimated on gated subsets using an “upsampling factor” parameter [observed # events x mean (upsampling factor)]. Gating of normal subsets was completely automated using a DNN classifier parameter. In 96% of positive cases, MRD was rapidly identified as immunoglobulin light chain-restricted plasma cells on a single pre-calculated UMAP plot (tube 2), after dual coloring for kappa and lambda. On the remainder 4% of positive cases, MRD was easily segregated from benign plasma cells using the AI-generated “aberrancy scale” parameter or other strategies. The use of AI-enhanced files reduced manual analysis time from 15.05 minutes (SD = 6.84) to 1.29 minutes (SD = 0.90) per case, on average (p < 0.0001) (91% reduction of manual analysis time). MM MRD was detected in all positive cases above limit of detection for our assay (≥0.0002%), with excellent quantitative correlation with conventional analysis on original FC files (linear regression slope = 0.9841, P < 0.0001). Conclusion: We introduce a largely unsupervised AI pipeline that transforms raw MM MRD FC data from the Euroflow Consortium panel into a markedly smaller, AI-annotated and software-agnostic FC file, including comparison to normal controls. This CCADDAS pipeline simplifies and accelerates detection of MM MRD in clinical diagnostics, reducing the number of cells analyzed by 95% and manual analysis time by 91%, without impacting test performance. Moreover, CCADDAS can be utilized for any laboratory-developed MM MRD assay, and its small-sized export is compatible with any clinical FC software, computing platform, and analysis strategy. Adoption of CCADDAS is likely to facilitate the implementation of MM MRD FC analysis by more clinical laboratories.

Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4′,6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining–scanning–bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining–scanning–bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts’ annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.