Library design for multiplexed CRISPR screens to assess barcode swapping

Calderon Lab Meeting

November 6, 2025

Noah Gordon

PhD Student, Bioengineering

University of California, Berkeley

University of California, San Francisco

Library design for multiplexed CRISPR screens to assess barcode swapping

Calderon Lab Meeting

November 6, 2025

Noah Gordon

PhD Student, Bioengineering

University of California, Berkeley

University of California, San Francisco

Fraction of PICS autoimmunity SNPs (Farh et al., 2015)

This figure is from a paper cited by Diego Calderon in the second sentence of his PhD thesis.

“I wanted to first touch on the big-picture motivation for this work. \TODO gist: About 90% of phenotype-linked variants lie in non-coding regions of the genome. We believe these variants act to regulate the transcription and interaction between genes. Therefore, we are interested in studying gene-regulatory networks.”

Gene-enhancer associations (Fishilevich et al., 2017)

Ok so we’re interested in studying relationships between enhancers and genes, but this is not new… the novel

\TODO

Gene-enhancer associations (Fishilevich et al., 2017)

Gene-enhancer associations (Fishilevich et al., 2017)

“Although multiplex gene editing is possible with Cas9 nuclease, it requires relatively large constructs or simultaneous delivery of multiple plasmids, both of which are problematic for multiplex screens or in vivo applications.” (Zetsche et al., 2017)

Golden Gate assembly of single lentiviral vectors encoding CRISPR/Cas9 and multiple sgRNA expression cassettes. (Kabadi, Ousterout, Hilton, & Gersbach, 2014)

Enter Cas12A (Cofsky et al., 2020)

Cas12A Library

ls | head -n 5

20251106_calderon_rotation.Rproj
apa.csl
assets
barcodes
berkeley_ucsf_bioe_logo.png

ls | head -n 5

20251106_calderon_rotation.Rproj
apa.csl
assets
barcodes
berkeley_ucsf_bioe_logo.png

ls | sort -r | head -n 5

upload.sh
top_right.png
top_right_logo.png
theme
teal_stripe_background.png

ls | head -n 5

20251106_calderon_rotation.Rproj
apa.csl
assets
barcodes
berkeley_ucsf_bioe_logo.png

ls | sort -r | head -n 5

upload.sh
top_right.png
top_right_logo.png
theme
teal_stripe_background.png

ls | sort | grep b | head -n 5

barcodes
berkeley_ucsf_bioe_logo.png
_brand.yml
broadgpp-dolcetto-targets-seta.txt
idt_library_cas12_screen.xlsx

depmap_K562_CRISPR_z_scores |> 
  filter(abs(z_score) > 3) |>
  arrange(gene_effect) |> 
  head(1)

  gene gene_effect   z_score      mean    stddev
1 ISCU   -3.299316 -3.034026 -2.017645 0.4224325

depmap_K562_CRISPR_z_scores |> 
  filter(abs(z_score) > 3) |>
  arrange(gene_effect) |> 
  head(71) |> 
  select(gene) |>
  write.table("depmap_essential_genes_70.tsv",sep = "\t", row.names = FALSE)

depmap_K562_CRISPR_z_scores |> 
  arrange(desc(gene_effect)) |> 
  head(20) |>
  select(gene) |>
  write.table("depmap_proliferative_genes_20.tsv",sep = "\t", row.names = FALSE)

depmap_K562_CRISPR_z_scores |> 
  arrange(abs(gene_effect)) |> 
  head(30) |>
  select(gene) |>
  write.table("depmap_non-essential_genes_20.tsv",sep = "\t", row.names = FALSE)

# CONSTANTS FROM HSUING ET AL
WTDR <- "AATTTCTACTCTTGTAGAT"
DR1 <- "AATTTCTACTGTCGTAGAT"
DR3 <- "AATTTCTACTCTAGTAGAT"
DR8 <- "AATTTCTCCTCTAGGAGAT"
DR10 <- "AATTCCTACTCTCGTAGGT"

# LIBRARY CONSTANT REGIONS
CLONING_SITE <- "GTAGATCGAGACGGTTGTATTTGTACGTCTCAAATTTCTCCTCT"
END_HOMOLOGY <- "ACTGCTTTTTGCTTGTACT"


# Database sources for barcodes, guides

#barcodes generated using https://github.com/finkelsteinlab/freebarcodes
barcodes <- readLines("barcodes/barcodes12-2.txt")

# dolcetto from https://www.addgene.org/pooled-library/broadgpp-human-crispri-dolcetto/
# dolcetto file: https://media.addgene.org/cms/filer_public/1c/59/1c59fe51-ef6e-44bf-963a-598edadcd66f/broadgpp-dolcetto-targets-seta.txt
dolcetto <- read.delim("broadgpp-dolcetto-targets-seta.txt", stringsAsFactors = FALSE)


dolcetto_guides_from_genes <- function(dolcetto_df, genes, group_name) {
  dolcetto_df %>%
  filter(Annotated.Gene.Symbol %in% genes$gene) %>%
  select(-Annotated.Gene.ID) %>%
  rename(
    gene = Annotated.Gene.Symbol,
    guide = Barcode.Sequence
  ) %>%
  mutate(group = group_name) %>%
  arrange(gene) %>%
  group_by(gene) %>%
  mutate(guide_num = row_number()) %>%
  ungroup() %>%
  pivot_wider(
    id_cols = c(gene, group),
    names_from = guide_num,
    values_from = guide,
    names_prefix = "guide"
  ) %>%
  select(group, gene, starts_with("guide"))
}

depmap_70_essential <- read.table("depmap_essential_genes_70.tsv", sep = "\t", header = TRUE)
essential <- dolcetto_guides_from_genes(dolcetto, depmap_70_essential, "essential")

depmap_20_nonessential <- read.table("depmap_non-essential_genes_20.tsv", sep = "\t", header = TRUE)
nonessential <- dolcetto_guides_from_genes(dolcetto, depmap_20_nonessential, "non-essential")

ntc <- dolcetto %>%
  # Filter only "NO-TARGET" rows
  filter(Annotated.Gene.Symbol == "NO-TARGET") %>%
  # Drop unnecessary column
  select(-Annotated.Gene.ID) %>%
  # Rename columns
  rename(
    gene = Annotated.Gene.Symbol,
    guide = Barcode.Sequence
  ) %>%
  # Add a group column (optional)
  # Arrange by gene (though all are "NO-TARGET")
  arrange(gene) %>%
  # Assign group numbers for every 3 rows
  mutate(group_id = (row_number() - 1) %/% 3 + 1) %>%
  # Number guides within each group
  group_by(group_id) %>%
  mutate(guide_number = row_number()) %>%
  ungroup() %>%
  # Pivot wider so each group becomes one row
  pivot_wider(
    id_cols = group_id,
    names_from = guide_number,
    values_from = guide,
    names_prefix = "guide"
  ) %>%
  
#ok now we have essential, nonessential, and ntc as dfs with these 5 columns: 
#group, gene, guide1, guide2, guide3

full_library <- bind_rows(essential, nonessential, ntc)
full_library <- bind_cols( full_library, barcode = barcodes[1:nrow(full_library)]) 
full_library <- full_library %>%
  mutate(dr1 = WTDR) %>%
  mutate(dr2 = DR1) %>%
  mutate(cs = CLONING_SITE) %>%
  mutate(end_homol = END_HOMOLOGY ) %>%
  select(group, gene, dr1, guide1, dr2, guide2, cs, barcode, end_homol)
  
idt_library <- full_library %>%
  mutate(
    sequence = paste0(dr1, guide1, dr2, guide2, cs, barcode, end_homol),
    `Pool name` = "cas12_essentiality_screen"  # replace with the desired pool value
  ) %>%
  select(`Pool name`, sequence)

write_xlsx(idt_library, "idt_library_cas12_screen.xlsx")

essential |> head(1)

# A tibble: 1 × 5
  group     gene    guide1               guide2               guide3            
  <chr>     <chr>   <chr>                <chr>                <chr>             
1 essential ATP6V0C CGAGGACGGGCCGGGCACGA CGGGCACGAAGGTTTGCGGG GGGCCGGGCACGAAGGT…

full_library |> select(dr1, guide1, dr2, guide2, cs, barcode, end_homol) |> head()

# A tibble: 6 × 7
  dr1                 guide1               dr2    guide2 cs    barcode end_homol
  <chr>               <chr>                <chr>  <chr>  <chr> <chr>   <chr>    
1 AATTTCTACTCTTGTAGAT CGAGGACGGGCCGGGCACGA AATTT… CGGGC… AATT… AACAAC… AATTCCTA…
2 AATTTCTACTCTTGTAGAT GCCTTCGAGTCCTGGGGCGG AATTT… GCGGC… AATT… AACAAC… AATTCCTA…
3 AATTTCTACTCTTGTAGAT GACGCGGCGCTGCTCCATGG AATTT… GCGCG… AATT… AACAAG… AATTCCTA…
4 AATTTCTACTCTTGTAGAT CAGCCCTCGCGTGTACCTGA AATTT… CCTGA… AATT… AACACC… AATTCCTA…
5 AATTTCTACTCTTGTAGAT ACCGAAAGGGCCATGACGCG AATTT… GAAAG… AATT… AACAGG… AATTCCTA…
6 AATTTCTACTCTTGTAGAT CACCGAATTGAAGAGCATCA AATTT… GCGAT… AATT… AACCAC… AATTCCTA…

full_library |> select(dr1, guide1, dr2, guide2, barcode, cs, end_homol) |> head()

# A tibble: 6 × 7
  dr1                 guide1               dr2    guide2 barcode cs    end_homol
  <chr>               <chr>                <chr>  <chr>  <chr>   <chr> <chr>    
1 AATTTCTACTCTTGTAGAT CGAGGACGGGCCGGGCACGA AATTT… CGGGC… AACAAC… AATT… AATTCCTA…
2 AATTTCTACTCTTGTAGAT GCCTTCGAGTCCTGGGGCGG AATTT… GCGGC… AACAAC… AATT… AATTCCTA…
3 AATTTCTACTCTTGTAGAT GACGCGGCGCTGCTCCATGG AATTT… GCGCG… AACAAG… AATT… AATTCCTA…
4 AATTTCTACTCTTGTAGAT CAGCCCTCGCGTGTACCTGA AATTT… CCTGA… AACACC… AATT… AATTCCTA…
5 AATTTCTACTCTTGTAGAT ACCGAAAGGGCCATGACGCG AATTT… GAAAG… AACAGG… AATT… AATTCCTA…
6 AATTTCTACTCTTGTAGAT CACCGAATTGAAGAGCATCA AATTT… GCGAT… AACCAC… AATT… AATTCCTA…

Cas12 Pilot

Source: Kina

Show flow data

What’s next?

Source: Kina

Source: Kina

Thank you!

[1]. Cofsky, J. C., Karandur, D., Huang, C. J., Witte, I. P., Kuriyan, J., & Doudna, J. A. (2020). CRISPR-Cas12a exploits r-loop asymmetry to form double-strand breaks. Elife, 9, e55143.

[2]. Cohen, J. (2018). CRISPR too fat for many therapies — so scientists are putting the genome-editor on a diet. Science. Retrieved from https://www.science.org/content/article/crispr-too-fat-many-therapies-so-scientists-are-putting-genome-editor-diet

[3]. Farh, K. K.-H., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W. J., Beik, S., Shoresh, N., Whitton, H., Ryan, R. J. H., Shishkin, A. A., Hatan, M., Carrasco-Alfonso, M. J., Mayer, D., Luckey, C. J., Patsopoulos, N. A., De Jager, P. L., Kuchroo, V. K., Epstein, C. B., Daly, M. J., Hafler, D. A., & Bernstein, B. E. (2015). Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature, 518(7539), 337–343. https://doi.org/10.1038/nature13835

[4]. Fishilevich, S., Nudel, R., Rappaport, N., Hadar, R., Plaschkes, I., Iny Stein, T., Rosen, N., Kohn, A., Twik, M., Safran, M., & others. (2017). GeneHancer: Genome-wide integration of enhancers and target genes in GeneCards. Database, 2017, bax028.

[5]. Kabadi, A. M., Ousterout, D. G., Hilton, I. B., & Gersbach, C. A. (2014). Multiplex CRISPR/Cas9-based genome engineering from a single lentiviral vector. Nucleic Acids Research, 42(19), e147–e147.

[6]. Liebler, J. (2014). CRISPR–Cas9 in complex with guide RNA and target DNA. Online image, Art of the Cell. Retrieved from https://www.artofthecell.com/wp-content/uploads/2014/06/Art-of-the-Cell-CRISPR-Cas9-in-Complex-with-Guide-RNA-and-target-DNA.jpg?w=1920&ssl=1

[7]. Zetsche, B., Heidenreich, M., Mohanraju, P., Fedorova, I., Kneppers, J., DeGennaro, E. M., Winblad, N., Choudhury, S. R., Abudayyeh, O. O., Gootenberg, J. S., & others. (2017). Multiplex gene editing by CRISPR–Cpf1 using a single crRNA array. Nature Biotechnology, 35(1), 31–34.