Mahdokht Masaeli

Quantitative Diagnosis of Malignant Pleural Effusions by Single-Cell Mechanophenotyping

Single-cell biophysical properties were used for diagnosing malignant pleural effusions from patients. Cytometry Device Helps (De)form a Diagnosis Is it benign, or malignant? That is the main concern of cytopathologists as they screen cells in pleural effusions, taken from the lungs of patients suspected of having infections or cancer. This process is subjective and time-intensive and requires an expert’s eye. So, to quickly “prescreen” samples for malignancy (and follow-up), Tse et al. describe deformability cytometry (DC)—an approach that relies on microfluidic forces to diagnose pleural effusion samples as malignant, or not. The authors’ device accelerates effusion samples through two opposing microfluidic channels. At the channels’ four-way intersection, the cells are rapidly decelerated as they encounter the opposing flow, and then exit out the side channels. This leads to cell deformation, changing them from sphere-like shapes to pancakes. High-speed video of this intersection allowed Tse et al. to quantify cellular squishing: the more deformable the cell, the more malignant it is. The authors took 119 pleural effusion samples from patients with known clinical outcomes—negative for malignant cells (benign), acute inflammation, chronic/mixed inflammation, atypical cells, and malignant pleural effusions (MPEs)—to develop a diagnostic scoring system on a scale of 1 to 10, with 1 being benign. DC showed the best predictive abilities in two high-confidence regimes: 1 to 6 and 9 to 10. Scores of 7 and 8 were more difficult to diagnose, so these may be the types of samples where a cytopathologist’s initial input would be necessary. Importantly, the authors looked at samples from patients that were cytology-negative with concurrent malignancy, such as a tumor, but 6 months later were diagnosed with disseminated disease. Five of 10 patients with high-grade cancers that were cytology-negative at sample collection scored high using DC. This suggests that the DC tool could be used to screen early for MPE. Using deformability as a marker of disease will require additional validation in pleural effusion samples from patients with many different types of cancer. Nevertheless, owing to the ease of use and objective readout, with further clinical testing, DC should be useful as a quick screening tool to form an early diagnosis of MPEs. Biophysical characteristics of cells are attractive as potential diagnostic markers for cancer. Transformation of cell state or phenotype and the accompanying epigenetic, nuclear, and cytoplasmic modifications lead to measureable changes in cellular architecture. We recently introduced a technique called deformability cytometry (DC) that enables rapid mechanophenotyping of single cells in suspension at rates of 1000 cells/s—a throughput that is comparable to traditional flow cytometry. We applied this technique to diagnose malignant pleural effusions, in which disseminated tumor cells can be difficult to accurately identify by traditional cytology. An algorithmic diagnostic scoring system was developed on the basis of quantitative features of two-dimensional distributions of single-cell mechanophenotypes from 119 samples. The DC scoring system classified 63% of the samples into two high-confidence regimes with 100% positive predictive value or 100% negative predictive value, and achieved an area under the curve of 0.86. This performance is suitable for a prescreening role to focus cytopathologist analysis time on a smaller fraction of difficult samples. Diagnosis of samples that present a challenge to cytology was also improved. Samples labeled as “atypical cells,” which require additional time and follow-up, were classified in high-confidence regimes in 8 of 15 cases. Further, 10 of 17 cytology-negative samples corresponding to patients with concurrent cancer were correctly classified as malignant or negative, in agreement with 6-month outcomes. This study lays the groundwork for broader validation of label-free quantitative biophysical markers for clinical diagnoses of cancer and inflammation, which could help to reduce laboratory workload and improve clinical decision-making.

Engineering fluid flow using sequenced microstructures

Controlling the shape of fluid streams is important across scales: from industrial processing to control of biomolecular interactions. Previous approaches to control fluid streams have focused mainly on creating chaotic flows to enhance mixing. Here we develop an approach to apply order using sequences of fluid transformations rather than enhancing chaos. We investigate the inertial flow deformations around a library of single cylindrical pillars within a microfluidic channel and assemble these net fluid transformations to engineer fluid streams. As these transformations provide a deterministic mapping of fluid elements from upstream to downstream of a pillar, we can sequentially arrange pillars to apply the associated nested maps and, therefore, create complex fluid structures without additional numerical simulation. To show the range of capabilities, we present sequences that sculpt the cross-sectional shape of a stream into complex geometries, move and split a fluid stream, perform solution exchange and achieve particle separation. A general strategy to engineer fluid streams into a broad class of defined configurations in which the complexity of the nonlinear equations of fluid motion are abstracted from the user is a first step to programming streams of any desired shape, which would be useful for biological, chemical and materials automation.

Multiparameter mechanical and morphometric screening of cells

We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply machine-learning approaches to discriminate different cell types and states. When employing the full 15 dimensional dataset, the technique robustly classifies individual cells based on their pluripotency, with accuracy above 95%. Rheological and morphological properties of cells while deforming were critical for this classification. We also show the application of this method in accurate classifying cells based on their viability, drug screening and detecting populations of malignant cells in mixed samples. We show that some of the extracted parameters are not linearly independent, and in fact we reach maximum classification accuracy by using only a subset of parameters. However, the informative subsets could vary depending on cell types in the sample. This work shows the utility of an assay purely based on intrinsic biophysical properties of cells to identify changes in cell state. In addition to a label-free alternative to flow cytometry in certain applications, this work, also can provide novel intracellular metrics that would not be feasible with labeled approaches (i.e. flow cytometry).