You also provided me with opportunities to take leadership, both in maintaining collaboration during my graduate project and in maintaining the lab's high safety standards as one of the lab's biosafety coordinators. Dennis, you were one of the first people I came into contact with at Caltech during the Chemical Biology recruitment dinners, and the interactions I had with you and other faculty helped seal my decision to choose Caltech for my graduate degree. I would also like to thank the former and current members of the Ismagilov group for their support over the past six years.
Throughout my time in the Ismagilov group, I had the opportunity to learn and become one of the lab's Biosafety Coordinators, along with Emily Savela and Eugenia Khorosheva. Additionally, I want to say a big thank you to all the individuals I have had the opportunity to work and interact with over the years, including Noah Ollikainen, Charlotte Lai, Elizabeth Soehalim, Elizabeth Detmar, Vicky Trinh, Chris Chen, Isabel Goronzy, and Inna-Marie Strazhnik. I also want to acknowledge the many Caltech staff that I have had the opportunity to interact with during my years at Caltech.
I first became acquainted with her through Michelle Hawley, one of the former directors of CSULA's Honors College program, after I was accepted into Caltech's PhD program in chemistry. Even though they didn't know much about science, they were always interested to know what I was working on, and I'd love to talk to them about it.
ABSTRACT
PUBLISHED CONTENT AND CONTRIBUTIONS
A.Q. Contributed to experiments for scSPRITE method development and validation (Figure 1)
LIST OF ILLUSTRATIONS AND/OR TABLES
INTRODUCTION
This approach captures DNA interactions over short distances, but misses many long-range DNA interactions, including inter-chromosomal DNA interactions. DNA loci within each core region are amplified, allowing the reconstruction of 3D DNA interactions without the need for nearby ligation. Bulk measurements have been useful for understanding general features about DNA organization, but they generally miss rare DNA contacts or the heterogeneity of DNA interactions from cell to cell.
However, it has generally been difficult to draw broad conclusions about single-cell chromosomal structure because scHi-C methods yield sparse intrachromosomal datasets at low resolutions. Consequently, genome-wide, high-resolution measurements of single-cell chromatin interactions have been difficult to achieve due to the limitations posed by proximity ligation. In this thesis, to provide an improved overview of single-cell DNA organization, I illustrate the development of a new single-cell method called single-cell recognition of split-pool interactions by label extension (scSPRITE)24.
Through scSPRITE, we are now able to measure all possible types of chromosomal structures, ranging from chromosomal territories and A/B compartments to structures not well studied with previous single-cell methods, such as TADs and interchromosomal hub interactions. In addition, scSPRITE is able to measure DNA interactions of thousands of cells simultaneously, allowing us to quantify the heterogeneity of interactions from cell to cell.
SINGLE-CELL MEASUREMENT OF HIGHER-ORDER 3D GENOME ORGANIZATION WITH SCSPRITE
Single-cell SPRITE (scSPRITE) works as follows: we dissociate cells into a single cell suspension, cross-link DNA and protein complexes in situ, isolate and permeabilize nuclei, digest DNA using an enzyme restriction and we perform two sets of separate and merged barcoding to (i) label DNA fragments contained in the same nucleus and (ii) label the 3D spatial arrangement of these fragments (Figure 1a). The resulting contact matrix for each single cell represents the number of clusters that contained each pair of 1 Mb bins across the genome. For each single cell in scSPRITE, we calculated the number of reads in each 1 Mb bin for each chromosome (chr1-19) genome wide.
Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large numbers of single cells. Number of contacts (top) and number of reads (bottom) obtained from scSPRITE (blue) and scHi-C16 (grey). Box plot, where whiskers represent 10th and 90th percentiles, box borders represent 25th and 75th percentiles, black line represents median, red dots represent individual cell examples (n = 1000 cells).
Right: single cell examples of chr1 and chr2 territories shown as number of DNA groups at 1 Mb resolution. Middle: box where whiskers represent 10th and 90th percentiles, box borders represent 25th and 75th percentiles, black line represents median, red dots represent individual cell examples (n = 1000 cells). Right: single-cell examples of A/B compartments in chr2:0-55 Mb plotted as numbers of DNA groups at 1 Mb resolution.
For box plots of each region: whiskers represent 10th and 90th percentiles, box borders represent 25th and 75th percentiles, black line represents median, red dots represent individual cell examples (n = 1000 cells). Box plot, where whiskers represent 10th and 90th percentiles, box borders represent 25th and 75th percentiles, black line represents median, red dots represent individual cell examples (n = 1000 cells). Additional unicellular examples of chromosome territory structure between chr1 and chr2; plotted as number of DNA groups at 1 Mb resolution.
Box plot represents normalized detection scores between chr1 and chr2, where whiskers represent 10th and 90th percentile, box limits represent 25th and 75th percentile, black line represents median, red dots represent samples of single cells (n = 1000 cells). Additional examples of single cell A/B compartments detected within 0-55Mb in chr2; plotted number of DNA clusters at 1 Mb resolution (right). Additional single cell examples of nucleolar interactions detected between chr18 and chr19; plotted number of DNA clusters at 1 Mb resolution; detection scores under contact card (right).
Single-cell examples of speckle interaction detected between chr2 and chr5; plotted number of DNA clusters at 1 Mb resolution. Additional single-cell examples of spot interactions detected between chr2 and chr4; plotted number of DNA clusters at 1 Mb resolution. Additional single-cell examples of centromere-proximal interactions detected between chr1 and chr11; plotted number of DNA clusters at 1 Mb resolution.
Box plot represents normalized detection scores between chr1 and chr11, where whiskers represent the 10th and 90th percentiles, box limits represent the 25th and 75th percentiles, black line represents the median, red dots represent single cell examples (n = 1000 cells).
