Cell streak imaging cytometry for rare cell detection

Fully integrated point-of-care blood cell count using multi-frame morphology analysis

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

Computational cytometer based on magnetically modulated coherent imaging and deep learning

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.

Ultra-high throughput detection (1 million droplets per second) of fluorescent droplets using a cell phone camera and time domain encoded optofluidics

Droplet-based assays-in which ultra-sensitive molecular measurements are made by performing millions of parallel experiments in picoliter droplets-have generated enormous enthusiasm due to their single molecule resolution and robustness to reaction conditions. These assays have great untapped potential for point of care diagnostics but are currently confined to laboratory settings due to the instrumentation necessary to serially generate, control, and measure tens of millions of droplets. To address this challenge, we have developed the microdroplet megascale detector (μMD) that can generate and detect the fluorescence of millions of droplets per second (1000× faster than conventional approaches) using only a conventional cell phone camera. The key innovation of our approach is borrowed from the telecommunications industry, wherein we modulate the excitation light with a pseudorandom sequence that enables individual droplets to be resolved that would otherwise overlap due to the limited frame rate of digital cameras. Using this approach, the μMD measures droplets at a rate of 10⁶ droplets per sec (ϕ = 166 mL h^-1) in 120 parallel microfluidic channels and achieves a limit of detection LOD = 1 μM Rhodamine dye, sufficient for typical droplet based assays. We incorporate this new droplet detection technology with our previously reported parallelized droplet production strategy, incorporating 200 parallel droplet makers and only one set of continuous and droplet phase inputs and one output line. By miniaturizing and integrating droplet based diagnostics into a handheld format, the μMD platform can translate ultra-sensitive droplet based assays into a self-contained platform for practical use in clinical and industrial settings.

Streak Imaging Flow Cytometer for Rare Cell Analysis

There is a need for simple and affordable techniques for cytology for clinical applications, especially for point-of-care (POC) medical diagnostics in resource-poor settings. However, this often requires adapting expensive and complex laboratory-based techniques that often require significant power and are too massive to transport easily. One such technique is flow cytometry, which has great potential for modification due to the simplicity of the principle of optical tracking of cells. However, it is limited in that regard due to the flow focusing technique used to isolate cells for optical detection. This technique inherently reduces the flow rate and is therefore unsuitable for rapid detection of rare cells which require large volume for analysis.To address these limitations, we developed a low-cost, mobile flow cytometer based on streak imaging. In our new configuration we utilize a simple webcam for optical detection over a large area associated with a wide-field flow cell. The new flow cell is capable of larger volume and higher throughput fluorescence detection of rare cells than the flow cells with hydrodynamic focusing used in conventional flow cytometry. The webcam is an inexpensive, commercially available system, and for fluorescence analysis we use a 1 W 450 nm blue laser to excite Syto-9 stained cells with emission at 535 nm. We were able to detect low concentrations of stained cells at high flow rates of 10 mL/min, which is suitable for rapidly analyzing larger specimen volumes to detect rare cells at appropriate concentration levels. The new rapid detection capabilities, combined with the simplicity and low cost of this device, suggest a potential for clinical POC flow cytometry in resource-poor settings associated with global health.

A computational streak mode cytometry biosensor for rare cell analysis

Streak mode imaging flow cytometry for rare cell detection involves imaging moving fluorescently labeled cells in the video mode with a CCD camera.

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.

Engineering opportunities in cancer immunotherapy

Immunotherapy has great potential to treat cancer and prevent future relapse by activating the immune system to recognize and kill cancer cells. A variety of strategies are continuing to evolve in the laboratory and in the clinic, including therapeutic noncellular (vector-based or subunit) cancer vaccines, dendritic cell vaccines, engineered T cells, and immune checkpoint blockade. Despite their promise, much more research is needed to understand how and why certain cancers fail to respond to immunotherapy and to predict which therapeutic strategies, or combinations thereof, are most appropriate for each patient. Underlying these challenges are technological needs, including methods to rapidly and thoroughly characterize the immune microenvironment of tumors, predictive tools to screen potential therapies in patient-specific ways, and sensitive, information-rich assays that allow patient monitoring of immune responses, tumor regression, and tumor dissemination during and after therapy. The newly emerging field of immunoengineering is addressing some of these challenges, and there is ample opportunity for engineers to contribute their approaches and tools to further facilitate the clinical translation of immunotherapy. Here we highlight recent technological advances in the diagnosis, therapy, and monitoring of cancer in the context of immunotherapy, as well as ongoing challenges.