Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer

Abstract

A compelling clinical need exists for inexpensive, portable haematology analyzers that can be utilized at the point-of-care in emergency settings or in resource-limited settings. Development of a label-free, microfluidic blood analysis platform is the first step towards such a miniaturized, cost-effective system. Here we assemble a compact lens-free in-line holographic microscope and employ it to image blood cells flowing in a microfluidic chip, using a high-speed camera and stroboscopic illumination. Numerical reconstruction of the captured holograms allows classification of unlabeled leukocytes into three main subtypes: lymphocytes, monocytes and granulocytes. A scale-space recognition analysis to evaluate cellular size and internal complexity is also developed and used to build a 3-part leukocyte differential. The lens-free image-based classification is compared to the 3-part white blood cell differential generated by using a conventional analyzer on the same blood sample and is found to be in good agreement with it.

Microscope-on-chip: combining lens-free microscopy with integrated photonics

“…In more recent work we demonstrate the performance of our Point-Source (PS) LHM prototype for a blood analysis application: a 3-part differential of unlabeled leukocytes [4]. The system uses a high-speed digital camera combined with a microfluidic channel and a photonic chip in an imaging configuration that achieves a sub-micron resolution ( Figure 2).…”

“…This means that a singlechannel imaging module of such a bench-top cytometer should acquire approximately 1000 images of cells per second. Such a single or multi-chip module consists of an illumination, µ-fluidics and imaging sub-system using the PS-LHM imaging configuration (Figure 4), similar to our prototype system described in [4]. The proposed fully integrated module, shown in Figure 4, is based on the following building blocks (designed and fabricated in our in-house clean-room facilities, using 200mm wafer processing for the photonic modules): -A light source, based on the out-of-plane out-coupling photonic structure [5], a custom designed grating that creates a virtual point source of light of few hundred nanometers in a predefined position above the microfluidic channel.…”

“…The sensor may produce data rates up to 65MB/s, which is to be taken into account in the overall system design. -A micro-fluidic channel, with sufficient hydrodynamic (or alternative) focusing capabilities (as described in more detail in [4]) that can achieve high-speed flow to reach the 1000 cell per second spec. These fluidic challenges are also being investigated but are outside the scope of this publication.…”

Label-free identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

“…This approach is especially powerful for high-dimensional data that are extremely difficult to manually process by a human due to the complicity and large size 38 . In biomedical research fields, machine learning methods have been widely used to solve complex biological problems: identification of bacterial species 39 , discrimination of WBC types 40,41 , and investigation of pathophysiologic conditions [42][43][44] . However, there has been no applications that use both 3-D RI tomography and machine learning for the purpose of biomedical studies.…”

Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

“…In the following section we discuss the working principle of the on-chip flow cytometer as it is conceived at imec [4]. Section III details the numerical implementation of the optical neural network and simulation results are presented and discussed in Section IV.…”