A portable battery powered microfluidic impedance cytometer with smartphone readout: towards personal health monitoring.

We present a portable system for personalized blood cell counting consisting of a microfluidic impedance cytometer and portable analog readout electronics, feeding into an analog-to-digital converter (ADC), and being transmitted via Bluetooth to a user-accessible mobile application. We fabricated a microfluidic impedance cytometer with a novel portable analog readout. The novel design of the analog readout, which consists of a lock-in-amplifier followed by a high-pass filter stage for subtraction of drift and DC offset, and a post-subtraction high gain stage, enables detection of particles and cells as small as 1 µm in diameter, despite using a low-end 8-bit ADC. The lock-in-amplifier and the ADC were set up to receive and transmit data from a Bluetooth module. In order to initiate the system, as well as to transmit all of the data, a user friendly mobile application was developed, and a proof-of-concept trial was run on a blood sample. Applications such as personalized health monitoring require robust device operation and resilience to clogging. It is desirable to avoid using channels comparable in size to the particles being detected thus requiring high levels of sensitivity. Despite using low-end off-the-shelf hardware, our sensing platform was capable of detecting changes in impedance as small as 0.032%, allowing detection of 3 µm diameter particles in a 300 µm wide channel. The sensitivity of our system is comparable to that of a high-end bench-top impedance spectrometer when tested using the same sensors. The novel analog design allowed for an instrument with a footprint of less than 80 cm2. The aim of this work is to demonstrate the potential of using microfluidic impedance spectroscopy for low cost health monitoring. We demonstrated the utility of the platform technology towards cell counting, however, our platform is broadly applicable to assaying wide panels of biomarkers including proteins, nucleic acids, and various cell types.

Tissue Cytometry With Machine Learning in Kidney: From Small Specimens to Big Data.

Advances in cellular and molecular interrogation of kidney tissue have ushered a new era of understanding the pathogenesis of kidney disease and potentially identifying molecular targets for therapeutic intervention. Classifying cells in situ and identifying subtypes and states induced by injury is a foundational task in this context. High resolution Imaging-based approaches such as large-scale fluorescence 3D imaging offer significant advantages because they allow preservation of tissue architecture and provide a definition of the spatial context of each cell. We recently described the Volumetric Tissue Exploration and Analysis cytometry tool which enables an interactive analysis, quantitation and semiautomated classification of labeled cells in 3D image volumes. We also established and demonstrated an imaging-based classification using deep learning of cells in intact tissue using 3D nuclear staining with 4',6-diamidino-2-phenylindole (DAPI). In this mini-review, we will discuss recent advancements in analyzing 3D imaging of kidney tissue, and how combining machine learning with cytometry is a powerful approach to leverage the depth of content provided by high resolution imaging into a highly informative analytical output. Therefore, imaging a small tissue specimen will yield big scale data that will enable cell classification in a spatial context and provide novel insights on pathological changes induced by kidney disease.