Rolling Out Cell Sorting with Microfluidics

Cell Sorting

Cells are quite valuable, especially when used for regenerative research, diagnostics or research. But harvested cells do not come presorted and need to be separated from a heterogeneous mixture of cells. There are already numerous methods to sort cells according to biophysical properties such as size, density, morphology, and dielectric or magnetic susceptibility. Cell sorting based on labels can have a higher specificity, but introduces extra steps to add and remove labels, which can affect the phenotype of the cell. Rohit Karnik of MIT has developed a cell sorting method based on cell rolling. The continuous, label-free process is described in “Cell sorting by deterministic rolling” in Lab on a Chip.

Cell Rolling

Target cells initiate cell rolling and enter the space between the ridges, which leads them to the gutter side. Non-target cells do not adhere or roll along the surface and maintain their original trajectories.

Target cells initiate cell rolling and enter the space between the ridges, which leads them to the gutter side. Non-target cells do not adhere or roll along the surface and maintain their original trajectories.

Cell rolling is a phenomenon where a cell is constantly forming and releasing adhesive bonds with a surface under fluid flow. The continuous creation and release of bonds by the cell induces rolling and is an integral role in the movement of lymphocytes, platelets, stem cells and metastatic cancer cells. To induce cell rolling, a surface needs to be coated with a ligand specific to the target cell type. The rolling target cells need to be focused, so slanted ridges are added to the bottom of the channel. When the target cell comes into contact with the surface of the coated ridges, it will begin to roll along it and eventually turn the corner into the space between ridges. By following the path of the direction of the ridges, the targeted cells will be focused on one side of the channel known as the gutter. The non-target cells should not adhere and roll along the ridges, allowing them to be spatially differentiated from the target cells. But the ridges actually serve an additional purpose: Acting as mixers, these ridges introduce circulation to the axial flow. This flow would normally be laminar, which would prevent the majority of cells from coming in contact with the surface of the channel and rolling.

Cell Rolling Testing & Optimization

HL60 (target) and K562 (non-target) cells enter through the same inlet. Due to the cell rolling sorting, the cells exit the two outlets highly organized.

HL60 (target) and K562 (non-target) cells enter through the same inlet. Due to the cell rolling sorting, the cells exit the two outlets highly organized.

Karnik validated this cell sorting method by processing the leukemia cell lines HL60 and K562. The surfaces were coated with P-selectin, which is a known ligand for the target HL60. HL60 and K562 were injected in a single inlet at a ratio of 2:3, respectively. Outlet A held 95.0 ± 2.8% HL60 cells, and outlet B had 94.3 ± 0.9% K562 cells. The cell sorting was extremely successful and 87.2 ± 3.7% and 76.7 ± 14.2% of the HL60 and K562 cells were recovered at the end of the process. Cell loss was most likely due to settling in the syringe at the inlet and cells remaining in the channel and dead volumes at the end of the process. Karnik also investigated the effect of the ligand concentration on cell sorting. Higher concentrations generated stronger cell-surface adhesion, but this came at the expense of cell rolling, so an operating point had to be determined for an ideal cell rolling concentration; at a flow rate of 70 µL/min, the channel was incubated with P-selectin concentration of 1.5 µg/mL.

Discussion

I really like this method of cell sorting because it is both passive and label-free. Although an extra section of channel must be added with coated ridges, no other major components are necessary. This method does not need any more equipment or chambers, making it simple to integrate into a project. With its small footprint, it can also be highly parallelized, negating the need for it to operate at high flow rates which could hinder cell rolling. This could either function as a standalone sorting device or integrated into a device processing a mixture of cells. Similar to other cell sorting procedures, widespread usage of this particular method is limited to availability of information: only cell lines for which we’ve characterized the rolling behavior for can be sorted this way.

Reference:

I really like this method of cell sorting because it is both passive and label-free. Although an extra section of channel must be added with coated ridges, no other major components are necessary. This method does not need any more equipment or chambers, making it simple to integrate into a project. With its small footprint, it can also be highly parallelized, negating the need for it to operate at high flow rates which could hinder cell rolling. This could either function as a standalone sorting device or integrated into a device processing a mixture of cells. Similar to other cell sorting procedures, widespread usage of this particular method is limited to availability of information: only cell lines for which we’ve characterized the rolling behavior for can be sorted this way.

 

Reference:

ResearchBlogging.org Choi, S., Karp, J., & Karnik, R. (2012). Cell sorting by deterministic cell rolling Lab on a Chip, 12 (8) DOI: 10.1039/c2lc21225k

Narrowing the Gap to Characterize Sickle Cell Disease

Microfluidic Future is by no means an accurate representation of all the current, ongoing research in microfluidics. Nevertheless, the fact that you won’t be able to find any articles about assays relying on a biophysical marker isn’t too far off the reality in microfluidics. I suppose this is partly due to the incredible amount of previous work on molecular markers when high resolution control hadn’t been realized yet. Regardless, I was happy to come across an article about a microfluidic device that indicates sickle cell disease risk using the disease’s biophysical characteristics. The work “A Biophysical Indicator of Vaso-occusive Risk in Sickle Cell Disease” appeared in Science Translational Medicine this past February and is a result of ongoing sickle research by MIT and Harvard Medical School. My friend originally forwarded me an article about it on Medgadget, which you should also check out, along with the podcast it mentions.

Sickle Cell Disease

Red blood cells with abnormal Hemoglobin (HbS) can form into a sickle shape and occlude blood vessels (image  source )

Red blood cells with abnormal Hemoglobin (HbS) can form into a sickle shape and occlude blood vessels (image source)

Sickle cell disease affects more than 13 million people worldwide and is responsible for $1.1 billion in costs per year in the United States. A mutation in the hemoglobin molecule causes red blood cells to change shape and stiffen when releasing oxygen. This shape change in many red blood cells can occlude a blood vessel, resulting in a crisis. While this fundamental component of the disease is known, there are many factors and processes relating to this event that are still unknown, resulting in an inability to discern the severity of sickle cell disease for a particular patient, besides the fact that they have it. The ability to predict the severity of the sickle cell disease would both aid the development of new therapies and guide clinical intervention.

Characterizing Disease Severity

The authors of this paper have previously demonstrated that they could simulate the vaso-occlusive crisis events by altering the oxygen concentration of sickle cell disease blood flowing through a capillary-sized microchannel. This paper takes it a step further and quantifies how the blood conductance, defined as velocity per unit pressure drop, changes during the events and uses it as a measure of disease severity. When the authors reduced the oxygen content, blood velocity would decrease, despite the constant pressure applied. The authors hypothesized that the conductance would change faster for patients with severe sickle cell disease as opposed to patients with a more benign form of the disease. You can see that the conductance of a patient with benign sickle cell disease (A) and that of a patient with severe sickle cell disease (B) are drastically different.

Blood from a patient with benign sickle cell disease (A) and sever sickle cell disease (B) were both subjected to changes in oxygen concentration, indicated by the top panels. This resulted in drastically different changes in conductance, which could distinguish the two types of patients.

Blood from a patient with benign sickle cell disease (A) and sever sickle cell disease (B) were both subjected to changes in oxygen concentration, indicated by the top panels. This resulted in drastically different changes in conductance, which could distinguish the two types of patients.

Device Value

As I mentioned, this device has potential use in developing therapies for sickle cell disease. The authors demonstrated this with 5-hydroxymethyl furfural (5HMF), which is known to increase hemoglobin oxygen affinity. Hemoglobin with a higher oxygen affinity would retain its ‘safe’ structure as it would release its oxygen less readily. As expected, this molecule caused a fivefold slower reduction in conductance change compared to an untreated, severe blood sample. While this device’s strength originates in its focus on biophysical markers, it could also be utilized to further understand the process of vaso-occlusive events and guide the handling of patients and discovery of effective therapies.

Regardless of the praise this paper has already received, I think it’s rather solid, and I’m not sure what else I would have liked to see addressed. Don’t expect to see this in your local pharmacy any time soon, though, since it can’t predict the occurrence of crises, but instead would indicate what treatment a patient would need.

Reference:

ResearchBlogging.org Wood DK, Soriano A, Mahadevan L, Higgins JM, & Bhatia SN (2012). A Biophysical Indicator of Vaso-occlusive Risk in Sickle Cell Disease Science Translational Medicine, 4 (123), 1-8 PMID: 22378926