Designing a fully integrated microfluidic proteomic sample preparation workflow for clinical applications

Mass spectrometry (MS) is pivotal in tumor characterization from clinical, patient-derived paraffin-embedded and formalin-fixed tissues (FFPE). However, implementation of the proteomic analysis in medium throughput clinical laboratories necessitate a lot of effort and is - although being partially automated – not cost effective. Widely used automated workflows on robotic platforms still cannot provide an intervention-free sample preparation optimized for clinical routine. Furthermore, transferring sample preparation workflows into clinics affords standardization and increases the bureaucratic workload while being not interoperable between laboratories. Thus, we opt to design a fully integrated miniaturized laser-microdissection-compatible sample preparation workflow using microfluidics following a "set-it-and-forget-it" principle. We validate our approach by elucidating proteomic intratumoral heterogeneity in breast cancer across different intrinsic subtypes. The automated processing is coupled to high throughput liquid chromatography and mass spectrometry (LC-MS). The herein developed workflow is applied for dissection and analysis of central, adjacent and peripheral regions of breast cancer tumors from 40 patients. With our labile surfactant-based protocol for miniaturization, we are able to extract up to 3000 proteins from microdissected paraffin-embedded and formalin-fixed tissue slices while reducing the variation across samples. Furthermore, our laser-microdissection and microfluidics-based workflow enables the proteomic analysis of breast cancer tumors for which alternative dissection approaches are not applicable. By dissection of central, adjacent and peripheral, we aim to display the intratumoral diversity on the proteome level. Thereby we currently opt to implement mass spectrometry-based approaches that spatially deconvolute the mechanisms underlying therapeutic resistance and recurrence in breast cancer. In our highly collaborative developmental process, we are able to show that proteomics is well on the way to resolve heterogeneity from patient derived FFPE samples with increased depth, easy handling combined in a robust workflow.