This invention relates to the use of CRISPR nucleic acids to screen for essential and non-essential genes and expendable genomic islands in bacteria, archaea, algae and/or yeast, to kill bacteria, archaea, algae and/or yeast, to identify the phenotype of a gene or genes, and/or to screen for reduced genome size and/or a gene deletion in bacteria, archaea, algae and/or yeast.

The subject matter disclosed herein is generally directed to methods and systems for screening phenotypes associated with genetic elements and identifying genetic elements at the single-cell level using optical barcodes. A major advantage offered by this approach is the ability to screen for any cellular phenotype that can be identified by high-resolution microscopy—including live-cell phenotypes, protein localization, or highly multiplexed expression profile and mRNA localization in conjunction with a large array of genetic elements applied as a pool in a single test volume.

The subject matter disclosed herein is generally directed to methods and systems for screening phenotypes associated with genetic elements and identifying genetic elements at the single-cell level using optical barcodes. A major advantage offered by this approach is the ability to screen for any cellular phenotype that can be identified by high-resolution microscopy—including live-cell phenotypes, protein localization, or highly multiplexed expression profile and mRNA localization in conjunction with a large array of genetic elements applied as a pool in a single test volume.

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

The present invention generally relates to imaging cells, for example, to determine phenotypes and/or genotypes in populations of cells, e.g., to build genotype-phenotype corresponse for high-throughput screening. In some cases, the cells may be manipulated, e.g., using CRISPR or other techniques. In certain embodiments, nucleic acids may be introduced to the cell, e.g., using a lentivirus. The nucleic acids may contain a guide portion comprising a DNA or RNA recognition sequence, a reporter portion, and an identification portion comprising one or more read sequences. The guide portion may be used to alter the phenotype of the cells, e.g., using a sequence, e.g., an sgRNA sequence, that can be targeted using CRISPR or other techniques, and in some cases, the phenotype of the cells may be determined using various imaging approaches. The identification portion may be determined using MERFISH or other suitable techniques. In addition, in some cases, association or colocalization between determination of the reporter and the read sequences may substantially improve decoding accuracy, e.g., due to lowered misidentification of background signals. Other aspects are generally directed to compositions or devices for use in such methods, kits for use in such methods, or the like.

Provided are a method for identifying functional elements of a genomic sequence and a library used for identifying functional elements of a genomic sequence.

This invention relates to recombinant Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) arrays and recombinant nucleic acid constructs encoding Type I-E CASCADE complexes, plasmids, retroviruses and bacteriophage comprising the same, and methods of use thereof for screening for variant cells of an organism.

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

The invention provides efficient methods for combining single-substitution libraries of nucleic acids that span and encode proteins of interest and for selecting resultant mutant proteins after expression which have improved properties or characteristics.

A HTP genomic engineering platform for improving filamentous fungal cells that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols is provided. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. Methods for isolating clonal populations derived from individual fungal spores are also provided.