This document describes the development of a novel single cell array system to isolate, sort, and analyze individual cells. The system uses magnetic fields to manipulate magnetically-labeled cells along paths etched on a silicon chip. Cells can be moved into storage compartments and extracted for analysis. The system aims to study rare cell populations like HIV-infected reservoirs. Engineering challenges involved creating automated control of magnetic fields and cell tracking via microscopy. Initial testing showed magnetic beads and cells can be reliably manipulated, though occasional clumping requires further optimization. Overall, the system allows long-term single-cell studies not possible with other techniques.
Classification of blast cell type on acute myeloid leukemia based on image mo...TELKOMNIKA JOURNAL
AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1.
Classification of blast cell type on acute myeloid leukemia based on image mo...TELKOMNIKA JOURNAL
AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1.
Role of cancer stem cells in cancer therapyniper hyd
Cancer is one of the leading cause of death, till to date there is no perfect treatment due to its stem cell relapse which is not responding to conventional therapies like chemo, radio, surgery. so we should target the cancer stem cell with novel targets like nano, and immunotherapies.
The ECIS is a turnkey system that provides researchers with an advanced, automated, non-invasive means to monitor cell behavior in real-time and without the use of labels.
International Journal of Stem Cell Research and Transplantation (IJST) is an international, Open Access, peer-reviewed journal, which mainly focuses, on the advancements made in the field of cell biology, specifically in the field of Stem Cells.
International Journal of Stem Cell Research and Transplantation (IJST) ISSN:2328-3548, is a free, Open Access, Peer-reviewed, exclusive online journal covering areas of Stem cell research, translational work and Clinical studies in the specialty of Stem Cells and Transplantation including allied specialties relevant to the core subject, which is dedicated in publishing high quality manuscripts.
International Journal of Stem Cell Research and Transplantation (IJST) is a peer-reviewed journal, and is dedicated to providing information with respect to the latest advancements that are being upgraded in our everyday life with respect to the application of Stem cells.
Role of cancer stem cells in cancer therapyniper hyd
Cancer is one of the leading cause of death, till to date there is no perfect treatment due to its stem cell relapse which is not responding to conventional therapies like chemo, radio, surgery. so we should target the cancer stem cell with novel targets like nano, and immunotherapies.
The ECIS is a turnkey system that provides researchers with an advanced, automated, non-invasive means to monitor cell behavior in real-time and without the use of labels.
International Journal of Stem Cell Research and Transplantation (IJST) is an international, Open Access, peer-reviewed journal, which mainly focuses, on the advancements made in the field of cell biology, specifically in the field of Stem Cells.
International Journal of Stem Cell Research and Transplantation (IJST) ISSN:2328-3548, is a free, Open Access, Peer-reviewed, exclusive online journal covering areas of Stem cell research, translational work and Clinical studies in the specialty of Stem Cells and Transplantation including allied specialties relevant to the core subject, which is dedicated in publishing high quality manuscripts.
International Journal of Stem Cell Research and Transplantation (IJST) is a peer-reviewed journal, and is dedicated to providing information with respect to the latest advancements that are being upgraded in our everyday life with respect to the application of Stem cells.
Finite Element Simulation of Microfluidic Biochip for High Throughput Hydrody...TELKOMNIKA JOURNAL
In this paper, a microfluidic device capable of trapping a single cell in a high throughput manner
and at high trapping efficiency is designed simply through a concept of hydrodynamic manipulation. The
microfluidic device is designed with a series of trap and bypass microchannel structures for trapping
individual cells without the need for microwell, robotic equipment, external electric force or surface
modification. In order to investigate the single cell trapping efficiency, a finite element model of the
proposed design has been developed using ABAQUS-FEA software. Based on the simulation, the
geometrical parameters and fluid velocity which affect the single cell trapping are extensively optimized.
After optimization of the trap and bypass microchannel structures via simulations, a single cell can be
trapped at a desired location efficiently.
Large scale cell tracking using an approximated Sinkhorn algorithmParth Nandedkar
Cell tracking for a large scale (of over 1 million cells) has not yet been achievable within reasonable a time scope with current NN/RNN/Bi-RNN based methods. This individual research conducted by me at Osaka University, ISIR seeks to solve this problem using the Sinkhorn algorithm, and taking inspiration from the MPM method (Hayashida, 2020)
Live cells respond to the changes of their physiological environment as well as to the mechanical stimuli occurring in and out of the cell body. It is known that cell directional motion is influenced by the substrate stiffness. A finite element modelling based on the tensegrity approach is used here to describe the biomechanical behavior of cells. The effects of substrate stiffness and prestress on strain energy of a cell are investigated by defining several substrate stiffness values and prestress values. Numerical simulations reveal that the internal elastic strain energy of the cell decreases as the substrate stiffness increases. As prestress of cell increases, the strain energy increases as well. The change of prestress value does not change behavior pattern of the strain energy: strain energy of a cell will decrease when substrate stiffness increases. These findings indicate that both cell prestress and substrate stiffness influence the cell directional movement.
2. 2
Abstract
The objective of this project is to create a system to isolate, sort, and control large arrays of
single cells, allowing for the retrieval of specific cells for downstream analysis. In large groups
of nearly homogenous cells, the averages mask the responses of small amounts of heterogeneous
cells which often provide crucial details necessary to understand their exact mechanisms. HIV
latency is a prime example; although HIV latency was discovered twenty years ago, what must
be done immunologically to clear infected reservoirs has yet to be resolved. To analyze what
causes these reservoirs, we create a platform to sort magnetically labeled cells into separate
compartments embedded upon a silicon wafer. By using a variety of wires and an external
magnetic field, we are able to control cell direction and efficiently sort them. In order to combat
HIV, and a larger class of diseases, we seek to create a device to isolate, store, and extract single
cells.
3. 3
1. Introduction
In the study of biological cells, the analysis of single cells and their pertinent interactions
is limited at best. Currently, the most popular method for single cell analysis, fluorescence
activated cell sorting, only provides a limited snapshot of a cell's life cycle (Herzenberg, Parks
and Sahaf). Because of this lack of resolution, mapping genomes and studying mechanisms of
rare cells often inhabiting largely homogenous cultures becomes difficult. By creating an array
for the sorting and retrieval of such cells, many cells can be analyzed for long periods of study.
This device would allow for the generation of accurate genomes for rare cells, and the study of
cell to cell interactions that would otherwise be impossible to observe with precision.
One important class of cells to study are those infected by human immunodeficiency
virus (HIV), a disease that affects approximately 35 million people. It has been known for
approximately twenty years that HIV undergoes a latent reaction (Chun and Fauci); however, the
exact mechanism that causes dormancy and reactivation has yet to be discovered (Ruelas and
Greene). Recently, histone deacetylase (HDAC) inhibitors such as vorinostat have shown
progress in disrupting latency, but the immunological process necessary to clear the reservoirs of
latent HIV infected cells is still poorly understood (Archin, Liberty and Kashuba) (Deeks and
Barré-Sinoussi). Creating an array to isolate, store, and analyze cells is essential to advances in
understanding the intricacies of not only HIV, but also other diseases as well (Yin and Marshall).
An understanding of CD8+
T cell responses, specific to HIV-1, is essential to controlling
and potentially curing the disease. These cells implement multiple methods to control viremia,
the spread of a virus through the bloodstream, such as the direct delivery of cytolytic proteins to
infected CD4+
and the secretion of multiple cytokines (Almeida, Sauce and Price) (David, Sáez-
4. 4
Cirión and Versmisse) (Chen, Piechocka-Trocha and Miura) (Betts, Nason and West). Some
patients, known as elite controllers, have strong immunological responses that are able to
suppress the replication of the virus and nearly halt the progression of HIV to AIDS.
Unfortunately, the mechanisms utilized by the elite controllers' immune systems are unclear due
to the wide variety of polyfunctionalities, proliferative capacities, and cytolytic capacities of
CD8+
T cells (Betts, Nason and West) (Migueles, Laborico and Shupert) (Zimmerli, Harari and
Cellerai) (Horton, Frank and Baydo). It is currently suspected that the direct degranulation of
serine proteases (e.g. perforin and granzyme B) is an especially effective method of HIV
suppression found in elite controllers (Migueles, Laborico and Shupert). Although there is
uncertainty about the exact mechanisms, CD8+
T cells are critical in the eradication of latent
reservoirs of HIV infected cells (Deeks and Barré-Sinoussi) (Migueles, Laborico and Shupert)
(Shan, Deng and Shroff). Because of this, analyzing the specific interactions between HIV-
specific CD8+
and infected cells is essential to the prevention, control, and cure of HIV.
Although this study will primarily be focusing on HIV, the single cell array can be
applied to a variety of problems such as cell lineage tracing (Meacham and Morrison)
(Kretzschmar and Watt), next generation vaccine development (Koff, Burton and Johnson),
tumorigenic potential of single tumor cells (Quintana, Shackleton and Sabel) (Navin, Kendall
and Troge), mechanism of aging and the cell life cycle (Rowat, Bird and Agresti) (Spencer,
Gaudet and Albeck), determining differentiation pathways of stem cells (Vermeulen, Todara and
Mello) (Kaern, Elston and Blake), cell decision making and cooperative behavior (Long, Tu and
Wang) (Balaban, Merrin and Chait) and gene regulation and correlated fluctuations (Stewart-
Ornstein, Weissman and El-Samad) (Toriello, Douglas and Thaitrong).
2. Methodology
5. 5
In order to move cells into and out of our array, we utilize the magnetic force. We begin by
creating paths of magnetic permalloy, Ni81Fe19, on a silicon wafer and magnetically labeling the
cells. The setup is situated in a horizontally rotating magnetic field, causing the magnetically
labeled cells to move in distinct steps according to the cycles of the field. By varying the
magnetic field at different locations our array we are able to cause cells to move in different
directions. To ensure the health of the cells, a complex system for temperature management must
be developed in conjunction with a nanoscope in order to track cell locations. The combination
of these parts into a silicon wafer is illustrated in Figure 1. My work on this project primarily
focused on instrument development regarding the physical structure and the automation of the
system created.
Figure 1: The array chamber (a) illustrates the cell compartments and the electronic switching
elements for varying placement of cells. The chip schematic (b) shows the overall layout of the
silicon wafer, including a deposition and extraction chamber. The entire chip is then contained
within an incubator (c) (Yellen).
2.1 Movement
6. 6
Our method of cell movement takes inspiration from magnetic bubble technology; an
uneven, magnetized path is used in combination with an external rotating magnetic field to create
motion. As the external field rotates, the points of lowest potential energy along the path move in
turn. This concept is illustrated in Figure 2.
Figure 2: A magnetically labelled cell, represented by the black dot, continually moves to a state
of lowest potential energy, represented by the blue region. The red regions represent regions of
highest potential energy, yellow medium levels, and green low to medium levels. This is
controlled by the rotating magnetic field represented by the red arrow (Yellen).
Because the pathing is magnetic, a cell remains attached to the permalloy pathing even if the
external magnetic field is exhibiting a pulling force. This attachment allows us to simultaneously
create cell movement at any angle.
2.2 Sorting and Storage
In order to sort and store the magnetically labeled cells, we utilize three types of
intersections, places where the permalloy paths meet or loop upon themselves. The first is
pictured in Figure 2; due to the geometry, the cell is able to jump the thin section of the vertical
path when traveling to the right, however, when going in the opposite direction, the cell instead
7. 7
travels along the adjacent path. This type of gate serves two primary functions; first it is used to
connect rows of cells as they are exiting the system. The connection greatly simplifies the
extraction process as they can be retrieved from a single location. Second, this type of
intersection can be built upon to create a basic containment system pictured in Figure 3. The
cells are able to cross the vertical path located in approximately the center of the diagram, but as
explained earlier, they are unable to exit; this allows for the cells to be trapped in a continuous
cycle.
Figure 3: A sample design of a storage compartment. A cell trapped within this compartment is
highlighted by the green circle. The cell is able to travel in a continuous loop until extracted
(Yellen).
In order to extract cells from these compartments, we build upon the first type of
intersection, pictured in Figure 2, by applying an additional magnetic force through the use of
wires. By applying current in the appropriate direction, we can create a magnetic force in line
with the overall magnetic field, causing cells to "hop over" the walls they would normally be
unable to climb. Because we are able to selectively apply a current to these wires at any given
time, they effectively become switches. Figure 4 demonstrates the pathing effects they have.
8. 8
Figure 4: The cell, represented by the black dot, is moving in the counter-clockwise direction.
The normal path expected is demonstrated in figures a through c. By applying a current to the
wire above the pathing, a magnetic force is created causing the expected path to change. This
new path is illustrated in figures d through f. In figures a and d the potential energy is illustrated
in which blue represents the lowest values and red the highest (Yellen).
In order to sort cells we utilize what is effectively a two way switch. Similar to the switch
pictured in Figure 4, a wire positioned above the setup has a current applied to it in order to
generate an additional magnetic force. The setup is pictured in Figure 5.
9. 9
Figure 5: Two adjacent paths are separated by a small gap. Because of the magnetization of the
paths, there is a symmetry in the points of lowest energy along both sides, as illustrated in figure
a. The expected pathing when no current is applied to the wire is pictured in figures a through c,
and the expected pathing when a current is applied is pictured in figures d through f. Figures a
and d show the potential energy, where dark blue is the lowest and red the highest (Yellen).
Without any additional force, a cell would simply loop around the path, but with a current
applied to the wire, a cell is able to hop the small gap. This hop occurs because of the symmetry
of lowest potential energy along the two paths. When the external magnetic field is in line with
the adjacent paths, the potential energy at either point is equal. By applying a small magnetic
force that is in line with the external magnetic field a cell is able to hop between the two points.
When these three gates are combined, we can create a complex array that allows for the
efficient sorting, storage and extraction of magnetically labeled cells. An example is pictured
below in Figure 6.
10. 10
Figure 6: An example design of an 8 by 8 array of cell storage compartments. Cells move along
the white lines, representing the permalloy pathing; the yellow and orange lines represent the
cell control wires. Cells initially enter in from the top left then move into the system where they
can exit in the lower right.
In order to prevent error and minimize the number of wires required to generate the
maximum number of compartments, each compartment contains what is essentially a two-step
lock, illustrated in Figure 6. Every row and column has an associated wire. In order for a cell to
enter or exit a compartment, both the wire associated with row and the wire associated with the
column must have a current applied to it.
11. 11
Figure 7: A close up image of the compartments pictured in Figure 6. Each compartment
requires both the wire associated with the row and the wire associated with the column to be
activated in order to enter or exit.
This allows multiple cells to be sorted at once without other cells accidentally entering
compartments on the same row or column as the intended destination.
2.3 Instrument Development
To create a reliable magnetic field and ensure the health of the cells under analysis, an
external structure must be created. This consists of a plastic support structure created by a 3D
printer, coils of copper wire, an incubation chamber and a nanoscope for the analysis of cell
locations. This setup is pictured in Figure 8.
12. 12
Figure 8: Through the use of two sets of copper wires wrapped perpendicularly too each other,
represented by the orange lines, a magnetic field is created. This setup is this contained within a
plastic support structure, represented by the gray and black, in which the slide and incubation
chamber are contained. The silicon wafer is contained within the center divot of the structure.
The implementation of this structure is pictured on the right where I nanoscope is used to
analyze cell locations.
The support structure is composed of four unique pieces. The magnetic field generating
component, pictured in Figure 9, is at the core. With two sets of copper wire coiled at right
angles to each other, the magnetic field’s direction can be rotated as they receive varying
currents.
13. 13
Figure 9: Opposite copper wires are powered in series to create a controllable magnetic field.
The orange strips represent the coils of copper wires.
In order to support the magnetic field generating structure and to hold the chip containing
the cells, a set of elbows and a center were created in a 3D printer. The elbows, one of which is
pictured in Figure 10, ensure the stability of both the magnetic field and the chip and allow for
the organization of the copper wires used to generate the magnetic field.
Figure 10: One of four elbow pieces used to support the structure.
The center component, pictured in Figure 11, rests on top of the entire structure. The
center is divoted, positioning the chip containing the cells in the center of the magnetic field.
Divots for input cables and clips are incorporated.
14. 14
Figure 11: The center of the structure. The indent within the center holds the silicon wafer, the
divot to the left allows for convenient wiring, and the holes on the right allow for the placement
of clips to stabilize the chip.
Finally, in order to keep the cells alive, an incubation chamber was used. By applying an
even electrical heating and measuring the chambers temperature, human body temperature can
be maintained. This setup encases the silicon wafer as pictured in Figure 1.
2.4 Automation
In order for the use of this system to be efficient, it is automated using a combination of
LabView to control a UEIDaq board and MATLAB to register cell locations through differential
imaging techniques. The interface of this system, in which voltage of wires, frequency of
rotation and the sorting of cells are all customizable is shown below in Figure 12.
15. 15
Figure 12: The user interface for sorting cells. The Resource and Device Information section is
data regarding the UEIDaq board used. Analog output settings allow for global customization of
Voltage and the length of a cycle, or a rotation of the magnetic field, in milliseconds. The
waveform chart and analog output display the voltages for each of the 32 pins. By selecting a
cell control map and anyone of the buttons A1 to H8, a cell can be sorted into the desired
compartment.
By using MATLAB, the entire system can be automated. Cell locations are determined in
each frame and the differences in locations over a series of frames are used to track speed and
ensure that any potential errors can be corrected.
16. 16
Figure 13: Using MATLab software, 5 micron beads moving along a track are identified. By
identifying cell locations, movement can be appropriately timed and predicted through a
LabView interface.
In order to maximize efficiency, cells will be sorted simultaneously. In order to accomplish
this, a map of the relative timings of the necessary gates to transfer a cell from the beginning of
the system to a given compartment is created. For example, sorting a cell into compartment A1,
requires the activation of three switches in sequential order. By generating a map containing each
of the compartments, the timings for all switches necessary to transfer a cell to any given
compartment can be efficiently retrieved. These timings can then be passed into the program
where an internal counter mechanism activates the switches when needed. Because we are able
to vary the frequency of rotation in the magnetic field, and therefore the speed of the cells,
timing is measured by the number of cycles instead of seconds.
3. Results
Efficacy of magnetic cell control is primarily being tested through the use of three and five
micron magnetic beads. Additionally, we have also shown efficacy using living cells illustrated
in Figure 13.
17. 17
Figure 14: A successful filming of a cell moving along the permalloy path. Each image is one-
fourth of a cycle later than the previous.
These beads approximate both the size and the magnetic moment of the magnetically labelled
HIV cells. In the majority of the initial testing of cell and bead manipulation, we found accurate
and reliable success, but occasionally some error emerged. This error manifested in two ways:
clumping and unexpected cell velocity.
In rare cases, a grouping of beads could agglomerate, causing a clump to become stuck
together and potentially fail to progress forward through the intersections. Although we expect
this will not be nearly as much of a problem in the case of the HIV-1 infected cells, due to
natural repulsion between biological cells, we are working to counteract this by increasing the
strength of the magnetic field while lowering the frequency when such cases occur. This
minimizes velocity error and increases the force on the cells, allowing them to begin to separate.
Additionally, by applying varying chemical coatings such as soap we are able to help prevent
these agglomerations.
18. 18
The majority of the clumping error is generated from unexpected velocities. As theory
suggests, we expect that the speed at which the cells move along the pathing varies linearly with
frequency of the external magnetic field. Experimentally, we find this pattern to hold true at low
frequencies, but as we approach values of approximately two hertz, the velocity of the cells
actually decreases and approaches zero as the frequency continually increases. This phenomenon
is illustrated in Figure 14. In order to ensure the continuous movement of all cells at expected
values, we utilize frequencies between .1 and 1 hertz.
Figure 15: The measured velocity of five micron beads is plotted against the frequency of the
rotation of the magnetic field. The velocity varies linearly as expected up until values of
approximately one hertz when we begin to see a decrease.
The automation of the external magnetic field and the UEIDaq controlled chip has proven
successful. We are able to precisely control the field’s strength and frequency at all points along
19. 19
with each of the pins used for sorting cells. Sample maps have functioned properly, but the final
mapping of the array is currently under construction.
The incubation chamber, similarly, has also been proven effective. We are able to clearly
view each of the beads or cells and accurately import their images into MATLAB for analysis
while maintaining a regular temperature of approximately 37˚ Celsius.
4. Future Work
Moving forward, there are two major steps in project progression. The first is completing the
automation of cell sorting. By utilizing the differential imaging software provided in MATLab
we are able to import images of cells from a nanoscope positioned above the system and analyze
cell locations. These locations can then be passed into the sorting program, allowing for
complete automation. Once this step is complete, the testing of efficacy of the entire system may
begin. Initially this will be done by conducting population tests with the three and five micron
beads, but live cell tests will be used soon afterwards. A large part of this stage will be
maximizing efficiency regarding the possibility of clumping and unexpected cell velocities.
Upon completion of the tool, tests upon the HIV-1 infected cells can begin. A variety of tests
will be implemented, but they will largely focus on the interactions between CD8+
HIV-1
specific cells and infected CD4+
cells. This will involve the efficacy of various methods of
elimination and the mapping of their respective genomes. The development of this tool will not
only allow for the better examination of HIV, but also a multitude of diseases and treatment
options.
5. Acknowledgments
20. 20
For their help with constructing and testing the device, I’d like to thank the researchers at
Duke, Dr. Benjamin Yellen, Roozbeh Abedini, Shahrooz Amin, Cody Baker, Dr. David
Murdoch, John Yi, and Zachary Healy. I’d like to thank to Dr. Jonathan Bennett and my
fellow Research in Physics Students for their assistance in learning the necessary material.
Finally, I’d like to thank the NCSSM Foundation for helping to fund my research.
21. 21
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