Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies

Abstract

In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.

FIG. 1.

Concept of “triple-shot” ESI-MS μTAS that dynamically analyzes the secretome, intracellular metabolome, and surfaceome of individual cells locally sampled from a bioreactor.

FIG. 2.

A schematic of the microfluidic dynamic sample platform for dynamic secretome analysis on a single-cell scale. The device includes localized probing of cell media from the individual cell surrounding; the extracted secretome is filtered via a size-selective nanoporous membrane diffusion to achieve low and high molecular weight separation of proteins in continuous flow. The processed sample containing the biomolecules within a target molecular weight range is then analyzed via ESI-MS.

FIG. 3.

Optical and SEM images of the silicon microfabricated, monolithically assembled lab-on-a-chip device for intracellular metabolome analysis, showing a microchannel for transport of cells enveloped by electrolysis electrodes (b), and the cell capturing array of pillars (cell trap) within the channel (c). The device operating sequence (d) includes the sample loading (i), cell capture/media rinsing (depicted in yellow) (ii), cell lysing/lysate infusion (iii), and reconditioning for repeated analysis (iv).

FIG. 4.

An overview of the steps required to analyze the surfaceome of a native cell via MS. Realization of these sample preparation steps in microfluidic format is an important challenge for advancing fast and sensitive surfaceome analysis.

FIG. 5.

Schematic of the potential parallel (a) and series (b) “Triple-Shot” ESI-MS μTAS. Depicted in each is a bioreactor, secretome processing device, intracellular device, and surfaceome processing device. In the parallel system, three samples are taken from similar regions of the bioreactor and brought to three separate microfluidic pathways for the secretome, intracellular metabolome, and surfaceome. In the series system, one sample is taken from the bioreactor where the media are processed by the secretome device, cells are then processed by the intracellular device, and finally, cell membranes are processed by the surfaceome device. $t_{\mathrm{tot}}$ represents the total amount of time for the sample preparation and analysis of the integrated system, $t_{\sec }$ represents the amount of time for sample preparation and analysis of the secretome, $t_{\mathrm{met}}$ represents the amount of time for sample preparation and analysis of the metabolome, and $t_{\mathrm{sur}}$ represents the amount of time for sample preparation and analysis of the surfaceome.