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J Exp Med (3): Single-Cell CRISPR Screening—Decoding Immune Heterogeneity and Causal Networks at Single-Cell Resolution

This article is “The Next Generation” CRISPR Chapter Three of “Screening and Causal Systems Immunology.”

J Exp Med : Why do we need “ Causal System Immunology ”？ ——CRISPR The historical context of screening

J Exp Med (2): Bulk CRISPR Screening platform —— From T Functional Analysis from the Cellular Level to the Entire Immune System

In the first two articles, we have seen that, Bulk CRISPR Screening has already enabled the identification of key immune‑regulatory genes at the population level. However, as research progresses, a fundamental question has become unavoidable: if the same gene performs entirely distinct functions across different immune cell subsets, can the mean still be considered reliable?

The answer is no.

This is precisely a single cell. CRISPR Filter ( scCRISPR ) The starting point of appearance —— It attempts to CRISPR It has evolved from a tool for measuring average effects into a causal system capable of dissecting differences in cellular fate.

01  From population averages to cellular heterogeneity: Bulk The issues identified through screening are beginning to surface.

Bulk CRISPR The essence of screening is to compress the responses of tens of thousands of cells into a single averaged signal. Yet the immune system is never average. Consider this outcome: after knocking out the same gene, 30% of the T Cellular functions are enhanced, 50% Almost unchanged, 20% Instead, it enters a state of exhaustion. In Bulk In the literature, all of this is often condensed into a single sentence: “The gene effect is not significant.” Yet the reality in biology is quite the opposite. —— It might be a The key switch that determines the fate‑determining cell lineage.

Thus, the issue becomes clear: what we lack is not more perturbations, but rather cellular‑level resolution to map those perturbations. scCRISPR It was precisely at this breakpoint that it emerged: it no longer asks whether a gene is important, but rather asks —— What exactly does this gene alter in every cell? 02 scCRISPR The core idea: simultaneously disentangle the sources of perturbations and the molecular states in each cell.

 

The most fundamental change in scCRISPR is actually quite simple: each cell no longer possesses just a transcriptome; instead, it simultaneously carries two types of information—what gRNA has perturbed it and what state it currently occupies. Consequently, a cell ceases to be a mere point and becomes a composite entity comprising perturbation, state, and fate trajectory.

This step is extremely critical, as it enables CRISPR For the first time, measurement shifted from the endpoint to process analysis. It is precisely under this very principle that, Perturb-seq Become scCRISPR The starting point.

scCRISPR The core technical challenge lies in: how to simultaneously capture… in single-cell sequencing. gRNA Information (knowing what has perturbed this cell) and transcriptomic information (knowing the state of this cell)? 03 Perturb-seq : let CRISPR The first piece of the puzzle in ushering in the single-cell era

 

The logic of Perturb‑seq is straightforward yet profoundly impactful: it seamlessly integrates CRISPR‑based perturbations with single‑cell RNA sequencing. The experimental workflow can be conceptualized as a continuous process:

 

 

Thus, for the first time, an experiment could answer: how does the entire transcriptional regulatory network reorganize after a specific gene is knocked out? This marks… CRISPR Filter from “ Find genes ” , enter “ Read system ”。04  The watershed of technology: achieving… gRNA Direct capture of

 

The history of scCRISPR is, at its core, an engineering‑driven evolutionary trajectory centered on the question of how to reliably read gRNAs.

Early methods (such as Perturb-seq、CRISP-seq ) Relies on indirect inference from barcodes, but lentiviral recombination leads to gRNA and barcode Mismatching renders the causal relationship unstable.

Subsequently appears CROP-seq , through LTR Structure lets gRNA Can be captured by transcription ” , which has resolved some issues, but efficiency remains limited.

The real leap forward occurred after the advent of direct capture strategies. ECCITE-seq With direct capture Perturb-seq By in 5' end or 3' The end‑direct design capture mechanism allows… gRNA It was initially incorporated into the sequencing workflow. Subsequently, more engineered approaches, such as probe hybridization, were developed.

What this evolution brings is not “ Progressive optimization ” , but rather a qualitative change: gRNA Capture from “ Inference ” Become “ Direct observation ” . This step makes scCRISPR It truly possesses large-scale system analysis capabilities.

 

| Technical Comparison Overview

 

05  When RNA No longer sufficient: scCRISPR Moving Toward Multimodality

As the technology matures, a new reality is becoming increasingly clear: looking only at… RNA alone is insufficient to explain cell fate. Consequently, scCRISPR has rapidly advanced into the multimodal phase.

At the chromatin level ( Perturb-ATAC、 sciATAC 、 Multiome Perturb-seq ) Let us see “ Where does regulation begin? ”；

At the protein level ( ECCITE-seq、CITE-seq、icCITE-seq ) Let us see “ Where does the functionality actually take place? ”；
      Even combined with TCR Clonal information enables the tracking of immune cell lineage fate. At this point, a cell is no longer merely an expression profile but rather a four‑layered system: scCRISPR First time starting to close in “ Real Immune System ” The structure.

 

06  Immunological Changes: From Gene Lists to Fate Networks

 

When After scCRISPR enters the immune system, it brings not “more genes,” but rather A completely new problem structure.

For example, in T In cell research, a classic shift is this: in the past, we used to say “ Certain gene regulatory pathways are depleted. ” Now we say: they regulate, at distinct stages, the transitions between quiescence maintenance, proliferative expansion, and terminal exhaustion—particularly in tumors. CD8⁺ T In cellular systems, scCRISPR Further reveals:

  • Tpex It is not a single state, but rather a dynamic flux among multiple subpopulations;
  • Exhaustion is not an endpoint, but rather a composite of multiple branching states;
  • Transcription factors are not “ Open / Close ” , but rather regulation “ Flow direction ”。
  • Similarly, in macrophages, microglia, Treg In the system, scCRISPR Continuously “ Marker Description ” Rewrite as “ State-Flow Network ” What it truly changes is a fundamental concept: immune cells are not a classification system, but rather a dynamical system.

Technical Challenges and Solution Paths: Approximating Causal Structures in the Presence of Noise

 

scCRISPR Once we enter the real immune system, its core bottleneck becomes increasingly apparent: perturbations. — Phenotypes are not directly observed; rather, they are reconstructed approximations derived from multiple layers of information loss. gRNA Capture instability, barcode loss, variations in editing efficiency, and mRNA/ Chromatin stochasticity and cellular state fluctuations together constitute structural noise, rendering moderate‑strength regulatory signals the most susceptible to being obscured.

Therefore, technological evolution proceeds along two parallel tracks: on one end, scaling up (targeted… Perturb-seq , double gRNA , composite index, whole genome), while the other end enhances accuracy (directly gRNA Capture and vector optimization); computational methods ( crispat 、 pertpy 、 CLEANSER etc.) further shifts from analytical tools to the noise-correction layer in causal reconstruction.

scCRISPR And thus from “ Tools for Measuring Perturbation Effects ” , gradually evolving into “ An Engineering Framework for Reconstructing Cellular Causal Networks in Noisy Systems ”。08 Summary

 

If we say Bulk CRISPR answers the question, “Which gene is important?” In contrast, scCRISPR for the first time addresses, “How does this gene alter the cell’s fate trajectory?” It transforms CRISPR from a mere “correlational tool” into a true “causal‑analysis system,” and propels immunology from “cell‑type characterization” toward an understanding of “cellular state dynamics.”

But the story is not yet over. For cells are never merely… “ What is it? ” , and also includes: “ Where is it, and who influences it? ” This is precisely the next one. —— Space CRISPR Select the questions to be answered.

 

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