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Biology subjects

Iram, S.

Publications and source records attributed to Iram, S..

3 recordsLinked to original sources

Catch bonds in sickle cell disease: shear-enhanced adhesion of red blood cells to laminin

Could the phenomenon of catch bonding--force-strengthened cellular adhesion--play a role in sickle cell disease, where abnormal red blood cell (RBC) adhesion obstructs blood flow? Here we investigate the dynamics of sickle RBCs adhering to a surface functionalized with the protein laminin (a component of the extracellular matrix around blood vessels) under physiologically relevant micro-scale flow. First, using total internal reflectance microscopy we characterize the spatial fluctuations of the RBC membrane above the laminin surface before detachment. The complex dynamics we observe suggest the possibility of catch bonding, where the mean detachment time of the cell from the surface initially increases to a maximum and then decreases as a function of shear force. We next conduct a series of shear-induced detachment experiments on blood samples from 25 sickle cell disease patients, quantifying the number and duration of adhered cells under both sudden force jumps and linear force ramps. The experiments reveal that a subset of patients does indeed exhibit catch bonding. By fitting the data to a theoretical model of the bond dynamics, we can extract the mean bond lifetime versus force for each patient. The results show a striking heterogeneity among patients, both in terms of the qualitative behavior (whether or not there is catch bonding) and in the magnitudes of the lifetimes. Patients with large bond lifetimes at physiological forces are more likely to have certain adverse clinical features, like a diagnosis of pulmonary arterial hypertension and intracardiac shunts. By introducing an in vitro platform for fully characterizing RBC-laminin adhesion dynamics, our approach could contribute to the development of patient-specific anti-adhesive therapies for sickle cell disease. The experimental setup is also easily generalizable to studying adhesion dynamics in other cell types, for example leukocytes or cancer cells, and can incorporate disease-relevant environmental conditions like oxygen deprivation. SIGNIFICANCEAbnormal red blood cell adhesion to the walls of blood vessels is a central feature of sickle cell disease. We study this adhesion by experimentally measuring how long on average red blood cells adhere to a protein-covered surface, and how the strength of the cell-protein bond depends on the force resulting from the surrounding fluid flow. The results vary widely from patient to patient, with some cases showing an unusual regime where the mean bond strength increases with force. We connect these measurements to clinical aspects of the disease, which may aid in the design of individualized therapies in the future.

biophysics↗

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells

Sickle cell disease (SCD), a group of inherited blood disorders with significant morbidity and early mortality, affects a sizeable global demographic largely of African and Indian descent. It is manifested in a mutated form of hemoglobin that distorts the red blood cells into a characteristic sickle shape with altered biophysical properties. Sickle red blood cells (sRBCs) show heightened adhesive interactions with inflamed endothelium, triggering obstruction of blood vessels and painful vaso-occlusive crisis events. Numerous studies have reported microfluidic-assay-based disease monitoring tools which rely on quantifying adhesion characteristics of adhered sRBCs from high resolution channel images. The current workflow for analyzing images from these assays relies on manual cell counting and detailed morphological characterization by a specially trained worker, which is time and labor intensive. Moreover manual counts by different individuals are prone to artifacts due to user bias. We present here a standardized and reproducible image analysis workflow designed to tackle these issues, using a two part deep neural network architecture that works in tandem for automatic, fast and reliable segmentation and classification into subtypes of adhered cell images. Our training utilized an exhaustive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. The automated image analysis performs robustly in comparison to human classification: accuracies were similar to or better than those of the trained personnel, while the overall analysis time was improved by two orders of magnitude.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biophysics↗

Controlling the speed and trajectory of evolution with counterdiabatic driving

The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context - counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.

evolutionary biology↗