http://hdl.handle.net/1721.1/54822 2017-07-09T21:36:32Z 2017-07-09T21:36:32Z Cermak, Nathan http://hdl.handle.net/1721.1/108830 2017-06-03T06:18:03Z 2017-01-01T00:00:00Z

Enabling high-throughput single-cell growth measurements with parallel microchannel resonators Cermak, Nathan Single cells constitute a fundamental unit of biological organization, yet most laboratory techniques are unable to characterize single cell behavior and instead measure average properties of many thousands of cells. Among cellular behaviors, one of the most fundamental is growth, in which a cell turns inanimate material into biomass, which is then used to further create more biomass. However, there are few tools available to study the process of single cells growing. In this thesis, I demonstrate a new method for observing and quantifying the growth of single cells in high-throughput. This method is applicable to any cell that can grow in suspension (including bacteria, yeast, and mammalian cells), is extremely precise, and directly measures single-cell biomass accumulation. This method utilizes an array of suspended microchannel resonators (SMRs), which are microcantilevers with an interior fluidic channel through which cells flow. Cells traversing the interior fluidic channel transiently change the mass of the microcantilever, changing its resonant frequency, which we measure. Previously, it has only been possible to operate a single SMR per microfluidic chip. To enable high-throughput growth measurements, we first developed scalable hardware to operate multiple SMRs on a single chip, utilizing frequency-division multiplexing and a digitally-implemented phase-locked loop array. We call this hardware MURC - the MUltiple Resonance Controller. Outside of operating SMRs, we envision that MURC may ultimately be useful for operating many resonator-based sensors (including mass, light, or force sensors). We next developed a microfluidic chip called the serial SMR array, consisting of 10- 12 SMRs interspersed along a single long microfluidic channel. Cells are periodically weighed as they flow through this channel, typically every 30 seconds or every two minutes. The change in cell mass between when a cell enters and exits the channel tells us how fast the cell is growing. Importantly, an entire queue of cells can transit this array nearly simultaneously, yielding growth rate measurements of hundreds of cells per hour. Compared to existing methods, this system is at least an order of magnitude more precise and provides similar or higher throughput. Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2017.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 119-130).

2017-01-01T00:00:00Z Romer, Katherine A http://hdl.handle.net/1721.1/107076 2017-02-23T07:17:45Z 2016-01-01T00:00:00Z

Deciphering the mitotic and meiotic phases of spermatogenesis in the mouse Romer, Katherine A Mammalian spermatogenesis includes two types of cell divisions. First, germ cells undergo transit-amplifying mitotic divisions, which enable prodigious output of mature spermatozoa. Second, they undergo reductive meiotic divisions to produce haploid gametes. In this thesis, I examine gene expression and regulation during the mitotic and meiotic phases of spermatogenesis. Chapter 2 describes how RA-STRA8 signaling regulates two key transitions: spermatogonial differentiation, which begins the transit-amplifying mitotic divisions, and meiotic initiation, which ends them. First, in mice lacking the RA (retinoic acid) target gene Stra8, undifferentiated spermatogonia accumulated; thus, Stra8 promotes spermatogonial differentiation as well as meiotic initiation. Second, injection of RA into wild-type males induced precocious spermatogonial differentiation and meiotic initiation; thus, RA acts instructively on germ cells at both transitions. Finally, competencies of germ cells to undergo spermatogonial differentiation or meiotic initiation in response to RA were found to be distinct and periodic. Chapter 3 describes a novel method for isolating precise populations of mitotic and meiotic germ cells from the mouse testis. We first synchronize germ cell development in vivo, and perform histological staging to verify synchronization. We then separate these germ cells from contaminating somatic and stem cells by FACS, to achieve ~90% purity of each distinct germ cell type, from the stem cell pool through mid/late meiotic prophase. Utilizing this "3S" method (synchronize, stage, and sort), we can robustly and efficiently separate germ cell types that were previously challenging or impossible to distinguish, with sufficient yield for transcriptomic and epigenetic studies. Chapter 4 presents a systematic comparison of the male and female gene expression programs of meiotic prophase. We performed transcriptional profiling of postnatal testes synchronized in precise stages of meiotic prophase, and compared to the same stages in the fetal ovary. We identified 260 genes up-regulated during both male and female prophase; this shared gene set represents a core meiotic program, composed of known and potential novel meiotic players. We also identified over two thousand genes that are up-regulated during meiotic prophase specifically in the male. These comprise both a male-specific meiotic program, and a preparatory program for cellular differentiation of spermatozoa. Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references.

2016-01-01T00:00:00Z Gura Sadovsky, Rotem http://hdl.handle.net/1721.1/104576 2016-10-01T06:25:50Z 2016-01-01T00:00:00Z

Measurement of rapid protein diffusion in the cytoplasm by photoconverted intensity profile expansion Gura Sadovsky, Rotem Whether at the level of a single protein, or in the cytoplasm as a whole, the diffusive mobility of proteins plays a key role in biological function. To measure protein diffusion in cells, researchers have developed multiple fluorescence microscopy methods, and have tested them rigorously. However, using these methods for precise measurement of diffusion coefficients requires expertise that can be a barrier to broad utilization of these methods. Here, we report on a new method we have developed, which we name Photo-converted Intensity Profile Expansion (PIPE). It is a simple and intuitive technique that works on commercial imaging systems and requires little expertise. PIPE works by pulsing photo-convertible fluorescent proteins, generating a peaked fluorescence signal at the pulsed region, and analyzing the spatial expansion of the signal as diffusion spreads it out. The width of the expanding signal is directly related to the protein ensemble mean-square displacement, from which the diffusion coefficient of the ensemble is calculated. In the main part of the thesis, we demonstrate the success of PIPE in measuring accurate diffusion coefficients in silico, in vitro and in vivo. We then broaden the discussion, and challenge the assumption that the Fickian diffusion equation is the most appropriate model for describing protein motion in the cytoplasm. Since the cytoplasm is crowded with obstacles that trap proteins for a wide range of times, the motion of those proteins may be more accurately described by models of anomalous diffusion. To contribute to the ongoing debate about anomalous diffusion, we show how PIPE can be used to measure the degree of diffusion anomality by examining the temporal scaling of the mean-square displacement. Whether for measuring normal or anomalous diffusion, we suggest that the simplicity and user-friendliness of PIPE could make it a useful tool in molecular and cell biology. Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 82-85).

2016-01-01T00:00:00Z Brock, Kelly Paige http://hdl.handle.net/1721.1/104575 2016-10-01T06:25:50Z 2016-01-01T00:00:00Z

Protein structure and interaction under environmental stress : from quality control recognition to evolution of collective behavior Brock, Kelly Paige A protein's function in the cell depends on its structure, which in turn depends on the intracellular environment. Stress like heat shock or nutrient starvation can alter intracellular conditions, leading to protein misfolding - i.e. the inability of a protein to reach or maintain its native conformation. Since many proteins interact with each other, protein misfolding and cellular stress response must be examined both on the scale of individual protein conformational changes and on a more global level, where interaction patterns can reveal larger-scale protein responses to cellular stress. On the individual scale, one example of a protein particularly susceptible to misfolding is the human von Hippel-Lindau (VHL) tumor suppressor. When expressed in the absence of its cofactors, VHL cannot fold correctly and is quickly degraded by the cell's quality control machinery. Here, I present a biophysical characterization of a VHL mutation that confers increased resistance to misfolding. Mathematical modeling provides an explanation for this mutant's increased stability in the cell by predicting how its cofactor and chaperone interaction sites are buried or exposed in the protein's predicted conformation. On a more global level, a budding yeast cell undergoing glucose deprivation both acidifies its cytosol and exhibits widespread protein clustering. By employing a proteome-wide computational assay, I examine how this drop in pH could lead to the formation of higher order protein structures. This modeling framework also provides a rationale for why these two related phenotypes might be beneficial, since protein clustering can help regulate relevant metabolic pathways and provide protection from protein misfolding and/or degradation. Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references.

2016-01-01T00:00:00Z