About me

Tania L. Gonzalez, PhD

Project Scientist at Cedars-Sinai Medical Center

Publications and Links


Current Work

Project Scientist, Cedars-Sinai Medical Center (4/2021-current)
Postdoctoral Scientist, Cedars-Sinai Medical Center (1/2016-4/2021)
Los Angeles, CA
  • Project scientist in the lab of Dr. Margareta Pisarska
    • Hybrid role: wetlab (perform experiments & train others) and drylab (bioinformatics)
  • Placenta gene expression, genetics, epigenetics, and cell biology
  • Next generation sequencing: human genome, transcriptome, methylome
  • Biosafety officer, BSL2+ biosafety forms & inventory, equipment set-up & maintenance
  • Pisarska lab website

Education

  • PhD: Molecular and Cell Biology, University of California, Berkeley (12/2015)
  • BS: Biochemistry and Molecular Cell Biology, University of California, Davis (6/2010)


Organisms studied

  • Humans
    • Transcriptomics: Single cell and single nuclei RNA-seq, ATAC-seq, and 10x Genomics multiomics; bulk RNA-seq (total RNA, mRNA, and small/microRNA sequencing); qRT-PCR validation
    • Epigenetics: DNA methylation analysis and annotation (Infinium MethylationEPIC array); LINE-1 assay (ELISA-based global methylation assay); nuclei isolation for ATAC-seq
    • Single cell isolation from human tissue: tissue dissociation; dead cell removal by antibody-based columns and fluorescence dye-based flow cytometry; basic microscopy to determine cell viability and yield; cryopreservation optimization to keep cells viable during frozen storage
    • Single nuclei isolation and troubleshooting for 10x Genomics multiomics sequencing, including experience with small samples and multinucleated cells. 
    • DNA/RNA/miRNA isolation from human tissue, plasma, and primary cells
      • Expert with small tissue samples and low volumes
      • Developed protocols for improved yield and quality
      • RIN > 8 (excellent RNA integrity for next generation sequencing)
      • Agarose gel electrophoresis to estimate RNA quality, analyze PCR, and isolate RNA isoforms and endogenous cDNA coding sequences
    • Protein experiments: crude protein extraction, SDS-PAGE gel electrophoresis, Western blot optimization, ELISA assays
    • Primary cell culture: fibroblast and epithelial cells
    • Human cell lines: HTR8/SVneo, BeWo, JEG-3, HEK293T
    • Cell culture experiments: molecular cloning for mammalian expression, gene knockdowns and overexpression, well coating and cell treatment, etc.
    • Biosafety level 2+ (BSL2+) regulations, inventory, and paperwork
      • Prepared lab for human samples of unknown COVID-19 status during the start of the pandemic (required upgrade from BSL2 to BSL2+ since we didn't know which samples could grow virus, so we had to be extra careful) 
  • Plants
    • Arabidopsis thaliana
    • Nicotiana benthamiana
    • Nicotiana tabacum
    • Genetics and RNA: Transformations, genotyping, DNA/RNA extraction, identifying novel splice products, molecular cloning, synthetic biology, plasmid design, de novo gene design, site directed mutagenesis
    • Protein biochemistry: inducible protein regulation, crude protein extraction, protein purification for protein-protein complex studies and antibody generation, Western blots
    • Dual protein fluorescence analysis: leaf scans, fluorescence data analysis
    • Effector-triggered immunity: host-pathogen responses and molecular mechanisms
  • Bacteria
    • Escherichia coli (E. coli) for molecular cloning and protein production
    • Agrobacterium tumefaciens for plant transformations
    • Geobacter sulfurreducens for riboswitch characterization; required strict anaerobic culture, glove boxes, removing oxygen from media, etc. See Kellenberger et al 2015.
    • Molecular cloning of plasmids 2.5 kb to 16 kb by TOPO A, restriction digest, and Gateway cloning
    • Fusion protein expression and purification
    • Microscopy
  • Yeast
    • Some experience with yeast cell culture while teaching undergraduate lab sections
    • Temperature-sensitive mutations and phenotypes
  • In vitro and general molecular techniques
    • Cell lysis for various purposes: DNA/RNA isolation, crude protein experiments, nuclei isolation
    • Cloning, computational preparation: BLAST, design specific primers for desired gene isoform, check for restriction enzymes in the template, check start/stop sites and reading frames.
    • Cloning, insert sequence into plasmid: high-fidelity PCR to amplify gene of interest, DNA insert isolation from agarose gel band, and integration into plasmid. E.coli transformation for plasmid amplification, single colony selection, colony PCR and miniprep, and Sanger sequencing analysis to identify clones with the correct insert.
    • Cloning, plasmid uptake into various cells by heat shock, electroporation, and lipid-based reagents (e.g. lipofectamine)
    • ELISA assays to quantify molecules
    • Fluorescence quantification from plant leaf scans, cell photos, gel bands, and qPCR. General knowledge of underlying biochemistry and math.
    • Gel electrophoresis and staining for DNA, RNA, and protein
    • Microscopy: trypan blue and fluorescent dyes for cell and nuclei microscopy
    • PCR: regular PCR, step-down PCR to amplify difficult products, ligation PCR, RT-PCR for splice factor analysis, colony PCR, qRT-PCR for gene expression quantification, site directed mutagenesis
    • Protein affinity chromatography to isolate tagged proteins (e.g. 6xHIS tag, HA tag) and any binding partners which can be identified by mass spectrometry.
    • Protein quantification assays: Bradford, BCA, and other colorimetric assays
    • Western blotting and related quality control techniques to determine equal sample loading (e.g. Coomasie Blue and Ponceau S staining)
    • Amazing sterile technique (sterile seed germination on sucrose plates, human cell culture, and bacteria that grow slower than E. coli all require above average sterilize technique)
    • Other: in vitro translation assays (limited experience)

Bioinformatics

  • NGS data annotation with various databases 
    • Ensembl/Biomart, miRBase, GENCODE, NCBI, Human Protein Atlas
  • Differential expression analysis 
    • DESeq2, edgeR
    • Batch correction, data integration
    • Multi-variable models ("correcting for covariables")
    • Single cell analysis (Seurat mostly, some Scanpy)
  • Statistical modeling, linear regressions, correlation modeling
    • Generalized linear models
    • Multiple regression analysis
    • Coding optimization for DNA methylation analysis (>865,000 rows of data)
    • Pearson correlations
    • Hidden Markov Models (HMM)
  • Clustering analysis
    • Multidimensional reduction (e.g. PCA, MDS, UMAP, tSNE) for bulk and single cell datasets
    • Heatmaps with dendrograms
    • Phylogenetic trees
    • k-means clustering
  • Gene enrichment analysis
    • Ingenuity Pathway Analysis, gene ontology (GO), KEGG, missMethyl
  • DNA methylation data annotation and visualization
    • Manhattan plots
    • Quantile-Quantile (QQ) plots
    • Pathway enrichment analysis that accounts for number of probed CpG sites per gene
  • Data visualization
    • ggplot2, plotly, matplotlib, circos plots (circlize), RColorBrewer, heatmaply, base R
    • Interactive plots, html widgets
  • Data filtering and annotation expert
    • [R] biomaRt, limma, tidyr, tidyverse, dplyr, reshape2
    • [Python] pandas, numpy, matplotlib
    • Integration of different datasets (e.g. methylation and RNA-seq data; miRNAs and genome context)
  • Sequence alignments 
    • BLAST, Clustal W and Omega, and various software (Lasergene Suite, BioEdit, etc)
    • Intron identification and alignment matching across species
    • Conserved sequence identification
  • Higher level mathematics and statistics (UC Davis, UC Berkeley courses)
    • Linear algebra
    • Differential equations
    • Cell biophysics
    • Physical chemistry
    • Enzyme kinetics
    • Mendelian genetics
    • Sequence alignment theory (e.g. molecular clock, gap open vs gap extension penalties)
    • Mathematical modeling and theory (e.g. eigenvalues, bathtub dynamics, Hidden-Markov Models, applications for evolution and ecology)
    • Structural biochemistry crystallography theory (UC Berkeley graduate course)

 

Programming languages 

  • R - use daily for data analysis, data visualization, and annotation
  • Linux command line - use to interact with Cedars-Sinai's high performance computing system, my home Linux server, and Linux-only bioinformatics tools
  • Perl - learned from the UC Davis bioinformatics intro course, still use for occasional NCBI database searches
  • Python - self-taught and used for data analysis when needed, e.g. when Python tools have advantages over R-based tools such as better memory usage for large datasets
  • (Linux bash .sh scripts) - introduced at Cedars-Sinai's high performance computing workshops, but I prefer to use a combination of Linux command line and Perl or Python or R scripts when possible.
  • (MATLAB) - learned in undergraduate bioinformatics research program, used for mathematical models of disease spread, don't use anymore
  • (Pascal) - learned in introduction to programming college course, don't use anymore
  • (JavaScript) - used from middle school to college for web design hobbies

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Last updated: October 2025
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