Therapies targeting signaling molecules mutated in cancers can often have striking short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures 1,2 . Resistance can result from a secondary mutations 3,4 , but other times there is no clear genetic cause, raising the possibility of non-genetic rare cell variability [5][6][7][8][9][10][11] . Here, we show that melanoma cells can display profound transcriptional variability at the single cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms Author contributions: SMS, AR designed the study. SMS performed all experiments and analysis except: MD, ST assisted with fluctuation analysis and RNA-sequencing; EAT, BE performed NGFR and AXL sort experiments; CK, MB, KS performed PDX experiments; PB, MH provided cell lines; MX performed WM989-A6 characterization; EE developed iterative RNA FISH protocol; INA, KN performed DNA sequencing. MH provided guidance. SMS, AR wrote the paper. Author information:AR receives consulting income and AR and SMS receive royalties related to Stellaris™ RNA FISH probes.

Visualizing the physical basis for molecular behavior inside living cells is a grand challenge in biology. RNAs are central to biological regulation, and RNA’s ability to adopt specific structures intimately controls every step of the gene expression program1. However, our understanding of physiological RNA structures is limited; current in vivo RNA structure profiles view only two of four nucleotides that make up RNA2,3. Here we present a novel biochemical approach, In Vivo Click SHAPE (icSHAPE), that enables the first global view of RNA secondary structures of all four bases in living cells. icSHAPE of mouse embryonic stem cell transcriptome versus purified RNA folded in vitro shows that the structural dynamics of RNA in the cellular environment distinguishes different classes of RNAs and regulatory elements. Structural signatures at translational start sites and ribosome pause sites are conserved from in vitro, suggesting that these RNA elements are programmed by sequence. In contrast, focal structural rearrangements in vivo reveal precise interfaces of RNA with RNA binding proteins or RNA modification sites that are consistent with atomic-resolution structural data. Such dynamic structural footprints enable accurate prediction of RNA-protein interactions and N6-methyladenosine (m6A) modification genome-wide. These results open the door for structural genomics of RNA in living cells and reveal key physiological structures controlling gene expression.

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

SUMMARY The brain produces two brain-derived neurotrophic factor (BDNF) transcripts, with either short or long 3′ untranslated regions (3′UTR). The physiological significance of the two forms of mRNAs encoding the same protein is unknown. Here we show that the short and long 3′UTR BDNF mRNAs are involved in different cellular functions. The short 3′UTR mRNAs are restricted to somata whereas the long 3′UTR mRNAs are also localized in dendrites. In a mouse mutant where the long 3′UTR is truncated, dendritic targeting of BDNF mRNAs is impaired. There is little BDNF in hippocampal dendrites despite normal levels of total BDNF protein. This mutant exhibits deficits in pruning and enlargement of dendritic spines, as well as selective impairment in long-term potentiation in dendrites, but not somata, of hippocampal neurons. These results provide insights into local and dendritic actions of BDNF and reveal a mechanism for differential regulation of subcellular functions of proteins.

RNA structure plays important roles in practically every facet of gene regulation, but the paucity of structural probes that function in vivo has limited current understanding. Here we design, synthesize, and demonstrate two novel chemical probes that enable selective 2’-hydroxyl acylation for accurate RNA structural analysis in living cells. RNA structure in embryonic stem cells and several other species is read out at single nucleotide resolution, revealing tertiary contacts and RNA-protein interactions.

SUMMARY Intensive efforts are focused on identifying regulators of human pancreatic islet cell growth and maturation to accelerate development of therapies for diabetes. After birth, islet cell growth and function are dynamically regulated; however establishing these age-dependent changes in humans has been challenging. Here we describe a multimodal strategy for isolating pancreatic endocrine and exocrine cells from children and adults to identify age-dependent gene expression and chromatin changes on a genomic scale. These profiles revealed distinct proliferative and functional states of islet α-cells or β-cells, and histone modifications underlying age-dependent gene expression changes. Expression of SIX2 and SIX3, transcription factors without prior known functions in the pancreas and linked to fasting hyperglycemia risk, increased with age specifically in human islet β-cells. SIX2 and SIX3 were sufficient to enhance insulin content or secretion in immature β-cells. Our work provides a unique resource to study human-specific regulators of islet cell maturation and function.

Molecular differences between individual cells can lead to dramatic differences in cell fate, such as death versus survival of cancer cells upon drug treatment. These originating differences remain largely hidden due to difficulties in determining precisely what variable molecular features lead to which cellular fates. Thus, we developed Rewind, a methodology that combines genetic barcoding with RNA FISH to directly capture rare cells that give rise to cellular behaviors of interest. Applied to BRAF V600E melanoma, we trace drug-resistant cell fates back to single-cell gene expression differences in their drug-naive precursors (initial frequency of ~1:1000–1:10,000 cells) and relative persistence of MAP-kinase signaling soon after drug treatment. Within this rare subpopulation, we uncover a rich substructure in which molecular differences between several distinct subpopulations predict future differences in phenotypic behavior, such as proliferative capacity of distinct resistant clones following drug treatment. Our results reveal hidden, rare-cell variability that underlies a range of latent phenotypic outcomes upon drug exposure.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. Yet it remains unclear whether these fluctuations can persist for much longer than the time for a single cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the time of a fluctuation of gene expression or cellular memory difficult to measure. Here, we report a method combining Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several cell divisions. MemorySeq revealed multiple gene modules that express together in rare cells within otherwise homogeneous clonal populations. Further, we found that these rare cell subpopulations are associated with biologically distinct behaviors in multiple different cancer cell lines, for example, the ability to proliferate in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations has the potential to reveal new forms of biological memory at the level of single cells and suggests that non-genetic heritability of cellular state may be a quantitative property. Main text:Cellular memory in biology, meaning the persistence of a cellular or organismal state over time, occurs over a wide range of timescales and can be induced by a variety of mechanisms. Genetic differences are one form of memory, encoding variation between organisms on multi-generational timescales. Within an organism, epigenetic mechanisms encode the differences between cell types in different tissues, with cells retaining memory of their state over a large number of cell divisions ( 1 ) . In contrast, recent measurements suggest that expression of many genes in single cells may have very little memory, displaying highly transient .