CRISPR as an Experimental Tool
How do we edit a specific gene to test exactly what it does?
💡 cuts a chosen DNA site directed by a guide RNA, but speed is not truth, so the result is trusted only after an search and a sequence-verified edit.
Prerequisite check
- A deletes a gene to see what breaks; a removes it only in chosen cells at a chosen time, so an animal survives long enough to be scored.
- uses a labeled probe to show which cells are transcribing a gene (it detects messenger RNA); uses an to show where a sits.
What you will learn
Goal: Students will explain how edits a chosen DNA site using a guide RNA, and identify the two checks (sequence-verify the edit, search for cuts) that make a experiment trustworthy.
- is programmable: a guide RNA matches a chosen and directs the Cas9 to cut there, and the cell's repair can be steered to knock a gene out, knock one in, or install a single point .
- To target a new site you only redesign the guide RNA, not rebuild a whole animal, so is far faster than older methods.
- means the guide also cut a near-match site elsewhere; the fix is to search the genome for cuts. Mosaicism means not every cell got edited; the fix is to sequence-verify the edit and report .
- The strongest causal proof is editing plus a rescue: re-add the correct gene and watch the defect disappear.
Model: Programmable scissors, and the two things that can fool you
is described in the research dossier as a programmable molecular scissors that cuts DNA at a sequence you specify with a guide RNA [DOI:10.1002/bdr2.2216]. A guide RNA is a short RNA whose letters match the exact DNA site you want to edit; it is the address label, and changing the guide changes which site gets cut. Cas9 is the cutting and only cuts where the guide RNA tells it to. After the cut, the cell repairs the break, and by controlling the repair scientists can knock a gene out, knock a new sequence in, or install a single precise point , far faster than old breeding-based methods.
The dossier names two failure modes and the gold-standard checks for each. : the guide RNA can stick to near-match sequences elsewhere and let Cas9 cut there too; if an cut, not your intended edit, caused the phenotype, your conclusion is wrong, so you deliberately search the genome for off-target cuts before trusting the result. Mosaicism: when you edit an , not every cell necessarily gets edited, so the animal is a patchwork of edited and unedited cells; you sequence-verify the edit (read the actual DNA letters) and measure how many alleles were modified. The same gold standard from Lesson 9 still applies: the strongest causal proof is editing plus a rescue.
Explore (work the model before reading on)
- Which part of decides where the cut happens? Which part does the cutting?
- What is an cut?
- If you wanted to test a different gene tomorrow, what is the only part of the system you would need to redesign, and why does that make CRISPR faster than building a new mouse?
- A teammate edits embryos, sees a , and announces the gene caused it. Name two specific ways they could be wrong and the check that catches each.
- Suppose you install the exact IRF6 linked to risk into a mouse and the mice develop clefts. A skeptic says maybe an cut did it. What experiment would you run to answer the skeptic and strengthen the causal claim?
- In one sentence, what pattern did your team find about what makes a result trustworthy, not just fast?
Guided notes
The pieces
- is a programmable editing tool: a guide RNA matches a chosen and directs the Cas9 to cut there, after which the cell's repair can knock a gene out, knock one in, or install a single point .
- The power is that to target a new site you only redesign the ____ (guide RNA), not rebuild an entire animal, so it is far faster than older methods.
Two checks are mandatory
- means the guide also cut a near-match site elsewhere; the fix is to deliberately search for cuts.
- Mosaicism means not every cell got edited; the fix is to sequence-verify the edit and report the (the percent of alleles actually modified).
Rescue closes the loop
- As in Lesson 9, the strongest causal proof is editing plus a ____ (rescue), where you re-add the correct gene and the defect disappears.
- Speed is not the same as truth: a asks is the gene necessary, lets you ask it precisely and fast, and a rescue closes the loop on causation, but every experiment still runs in a model animal, not in Mateo.
Reading the Research
- Skim the title and abstract first to get the gist.
- Circle the one sentence that states the main claim.
- Box the evidence the authors give for that claim.
- Mark one sentence that confuses you, and move on.
Vetted readings for this lesson
Track your progress today
Check these off as you work through the lesson, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
- Read the Model and answered the Explore questions.
- Filled in the guided notes in my own words.
- Defined the new vocabulary with an example.
- Built the producible: Write a one-paragraph CRISPR experiment plan to test whether the IRF6 regulatory risk variant can contribute to clefting in a mouse, naming the guide RNA target, the intended edit, one off-target check, one mosaicism (sequence-verification) check, and the rescue step, then explain why the mice clefted so the variant causes clefts is not yet a safe conclusion.
- Wrote my Claim, Evidence, and Reasoning exit ticket.
Exit ticket (Claim, Evidence, Reasoning)
- Claim: is a faster way than a traditional to test what a gene does.
- Evidence: To target a new site you only redesign the ____ (guide RNA), instead of breeding a whole new ____ ( animal).
- Reasoning: But the result is only trustworthy after you check for ____ () cuts and confirm the edit by ____ (), because either problem could cause a misleading phenotype.
| Criterion | Proficient | Developing | Beginning |
|---|---|---|---|
| Complete | Every required part of the artifact is present and filled in. | Most parts are present, but one is missing or left blank. | Several parts are missing. |
| Accurate | The science and data are correct and match the evidence. | Mostly correct, with a small factual slip. | Key science or data is wrong. |
| Scientific reasoning (CER) | States a claim, backs it with specific evidence, and explains the reasoning. | Has a claim and evidence, but the reasoning is thin or missing. | Gives an answer with no evidence or reasoning. |
| Professional communication | Clear, organized, and labeled the way a clinician or scientist would write it. | Readable but disorganized or missing labels. | Hard to follow. |
| Submitted | Turned in the right way (Schoology for routine work) and confirmed. | Turned in, but in the wrong place or unconfirmed. | Not turned in. |
- CompleteProficient: Nothing is left blank: the model fills every part of "Write a one-paragraph CRISPR experiment plan to test whether the IRF6 regulatory risk variant can contribute to clefting in a mouse, naming the guide RNA target, the intended edit, one off-target check, one mosaicism (sequence-verification) check, and the rescue step, then explain why the mice clefted so the variant causes clefts is not yet a safe conclusion.".
- AccurateProficient: Every number and claim matches the case evidence.
- Scientific reasoning (CER)Proficient: It names a claim, cites the specific evidence, and explains the reasoning, not just the answer.
- Professional communicationProficient: It is organized and labeled like a real chart note.
- SubmittedProficient: It would be turned in on Schoology and confirmed.
Where this leads: careers
What's next: We can now edit a gene precisely in a mouse, but Mateo is not a mouse. When is a mouse actually a good stand-in for him, and when does the species gap make the answer untrustworthy?